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物联网中英文对照外文翻译文献

中英文资料外文翻译Internet of Things1.the definition of connotationThe English name of the Internet of Things The Internet of Things, referred to as: the IOT.Internet of Things through the pass, radio frequency identification technology, global positioning system technology, real-time acquisition of any monitoring, connectivity, interactive objects or processes, collecting their sound, light, heat, electricity, mechanics, chemistry, biology, the location of a variety of the information you need network access through a variety of possible things and things, objects and people in the Pan-link intelligent perception of items and processes, identification and management. The Internet of Things IntelliSense recognition technology and pervasive computing, ubiquitous network integration application, known as the third wave of the world's information industry development following the computer, the Internet. Not so much the Internet of Things is a network, as Internet of Things services and applications, Internet of Things is also seen as Internet application development. Therefore, the application of innovation is the core of the development of Internet of Things, and 2.0 of the user experience as the core innovation is the soul of Things.2.The meaning of "material"Where the "objects" to meet the following conditions can be included in the scope of the "Internet of Things":1. Receiver have the appropriate information;2. Have a data transmission path;3. Have a certain storage capabilities;4. T o have the CPU;5．T o have the operating system;6. Have specialized applications;7. Have a data transmitter;8. Follow the communication protocol of Things;9. World Network, a unique number that can be identified.3. "Chinese style" as defined inInternet of Things (Internet of Things) refers to is the ubiquitous (Ubiquitous) terminal equipment (Devices) and facilities (Facilities), including with the "inner intelligence" sensors, mobile terminals, industrial systems, floor control system, the family of Intelligentfacilities, video surveillance systems, and external can "(Enabled), such as RFID, a variety of assets (the Assets), personal and vehicle carrying the wireless terminal" intelligent objects or animals "or" smart dust "(the Mote), through a variety of wireless and / or cable over long distances and / or short-range communication networks to achieve interoperability (M2M), application integration (the Grand Integration), and based on cloud computing, SaaS operation mode, in internal network (intranet), private network (e xtranet), and / or the Internet (Internet) environment, the use of appropriate information security mechanisms to provide a safe, controlled and even personalized real-time online monitoring, retrospective positioning, alarm linkage, command and control plan management, remote control, security, remote repair and maintenance, online upgrades, statistical reporting, decision support, the leadership of the desktop (showcase of the Cockpit Dashboard) management and service functions, "Everything," "efficient, energy saving, security environmental protection, "" possession, control, Camp integration [1].4.EU definitionIn September 2009, the Internet of Things and enterprise environments held in Beijing, China-EU Seminar on the European Commission and Social Media Division RFID Division is responsible for Dr. Lorent Ferderix, given the EU's definition of things: the Internet of Things is a dynamic global network infrastructure, it has a standards-based and interoperable communication protocols, self-organizing capabilities, including physical and virtual "objects" of identity, physical attributes, virtual features and smart interface and seamless integration of information networks . Internet of Things Internet and media, the Internet and business Internet one, constitute the future of the Internet.5．changeThe Internet of Things (Internet of Things) the word universally recognized at home and abroad Ashton, Professor of the MIT Auto-ID Center in 1999 first proposed to study RFID. The report of the same name released in 2005, the International T elecommunication Union (ITU), the definition and scope of the Internet of Things has been a change in the coverage of a larger expansion, no longer refers only to the Internet of Things based on RFID technology.Since August 2009, Premier Wen Jiabao put forward the "Experience China" Internet of Things was officially listed as a national one of the five emerging strategic industries, to write the "Government Work Report" Internet of Things in China has been the great concern of the society as a whole degree of concern is unparalleled in the United States, European Union, as well as other countries.The concept of Internet of Things is not so much a foreign concept, as it has been the concept of a "Made in China", his coverage of the times, has gone beyond the scope of the 1999 Ashton professor and the 2005 ITU report referred to, Internet of Things has been labeled a "Chinese style" label.6．BackgroundThe concept of Internet of Things in 1999. Internet-based, RFID technology and EPC standards, on the basis of the computer Internet, the use of radio frequency identification technology, wireless data communication technology, a global items of information to real-time sharing of the physical Internet "Internet of things" (referred to as the Internet of Things) , which is also the basis of the first round of the China Internet of Things boom set off in 2003.The sensor network is built up based on sensing technology network. Chinese Academy of Sciences in 1999 on the start sensor network research and has made some achievements in scientific research, the establishment of applicable sensor network.1999, held in the United States, mobile computing and networking International Conference, "The sensor network is a development opportunity facing humanity in the next century. In 2003, the United States, "T echnology Review" proposed sensor network technology will be future changes ten people's lives first.November 17, 2005, the WSIS held in Tunis (WSIS), the International T elecommunication Union released ITU Internet Report 2005: Internet of Things ", citing the concept of the" Internet of things ". The report pointed out that the ubiquitous "Internet o f Things" communication era is approaching, all the objects in the world, from tires to toothbrushes, from housing to the tissue via the Internet, take the initiative to be exchanged. Radio Frequency Identification (RFID), sensor technology, nanotechnology, intelligent embedded technology will be more widely used.According to the description of the ITU, the era of things, a short-range mobile transceivers embedded in a variety of daily necessities, human beings in the world of information and communication will receive a new communication dimension, from any time communication between people of the place of connection extended to the communication connection between persons and things and things and things. The Internet of Things concept of the rise, largely due to the International T elecommunication Union (ITU), the title of Internet of Things 2005 annual Internet Report. However, the ITU report the lack of a clear definition of Things.Domestic Internet of Things is also there is no single standard definition, but the Internet of Things In essence, the Internet of Things is a polymer application of modern information technology to a certain stage of development and technological upgrading of various sensing technology modern network technology and artificial intelligence and automation technology aggregation and integration of applications, so that the human and material wisdom of dialogue to create a world of wisdom. Because the development of the Internet of Things technology, involving almost all aspects of IT, innovative application and development of a polymer, systematic, and therefore be called revolutionary innovation of information industry. Summed up the nature of the Internet of Things is mainly reflected in three aspects: First, the Internet features that need to be networked objects must be able to achieve the interoperability of the Internet; identification and communication features, that is included in the Internet of Things "objects" must to have the functions of automatic identification and physical objects communication (M2M); intelligent features, the network system should have automated, self-feedback and intelligent control features January 28, 2009, Obama became the President of the United States, held with U.S.business leaders a "round table", as one of the only two representatives, IBM CEO Sam Palmisano for the first time that "the wisdom of the Earth" this concept, it is recommended that the new government to invest in a new generation of intelligent infrastructure.February 24, 2009 news, IBM Greater China CEO money crowd called "Smarter Planet" strategy announced in the forum 2009IBM.This concept was put forth, that is the great concern of the United States from all walks of life, and even analysts believe that IBM's vision is very likely to rise to U.S. national strategy, and caused a sensation in the world. IBM believes that the industry, the next phase of the mission is to make full use of the new generation of IT technology in all walks of life among specifically, is the embedded sensors and equipment to the power grid, railways, bridges, tunnels, highways, buildings, water supply systems dams, oil and gas pipelines and other objects, and is generally connected to the formation of Things.Strategy conference, IBM, and implant the concept of "wisdom" in the implementation of the infrastructure, strong, not only in the short term to stimulate the economy, promote employment, and in a short period of time for China to build a mature wisdom infrastructure platform.IBM "Smarter Planet" strategy will set off again after the wave of Internet technology industrial revolution. Former IBM CEO Lou Gerstner has raised an important point of view, every 15 years, a revolution in computing model. This judgment is the same as Moore's Law accurately call it a "15-year cycle Law". Before and after 1965, changes to the mainframe as a symbol, 1980 marked by the popularization of personal computers, 1995, the Internet revolution. Each such technological change are caused by the enterprise, industry and even the national competitive landscape of major upheaval and change. T o a certain extent in the Internet revolution is ripening by the "information superhighway" strategy. 1990s, the Clinton administration plan for 20 years, $ 200 billion to -4000 billion, construction of the U.S. National Information Infrastructure, to create a huge economic and social benefits.T oday, the "Smarter Planet" strategy by many Americans that there are many similarities with the "information superhighway", the same they revive the economy, a key strategy for competitive advantage. The strategy can be set off, not only for the UnitedStates, such as the Internet revolution was the wave of technological and economic concern, more attention from the world."Internet of Things prospects are very bright, it will dramatically change our current way of life." Demonstration director of the Center of Nanjing University of Aeronautics and Astronautics, National Electrical and Electronic Zhao Guoan said. Industry experts said that the Internet of things to our life personification of the things became a kind of human.Goods (goods) in the world of physical objects associated with each other "exchange", without the need for human intervention. The Internet of Things using radio frequency identification (RFID) technology, to achieve the interconnection and sharing of the automatic identification of goods (products) and information through the computer Internet. It can be said that the Internet of Things depict the world is full of intelligent. In the world of Internet of Things, material objects connected to the dragnet.The second session, held at Peking University in November 2008, China Mobile Government Seminar "Knowledge Society and Innovation 2.0", the experts made the mobile technology, the Internet of Things technology led to the development of economic and social form, innovative forms of change, and promote the The next generation of innovation for the knowledge society as the core of user experience (innovative 2.0) the formation of innovation and development of the form to pay more attention to the user to focus on people-oriented. Research institutions is expected to 10 years, the Internet of Things may be mass adoption of this technology will develop into one of thousands of yuan-scale high-tech market, the industry than the Internet 30 times.It is learned that the things industry chain can be broken down into the identity, perception, processing and information transfer, four links, each link of the key technologies for the wireless transmission network of RFID, sensors, smart chip and telecom operators. EPOSS in the "Internet of Things in 2020" report, an analysis predicted that the future development of the Internet of Things will go through four stages, 2010, RFID is widely used in the field of logistics, retail and pharmaceutical objects interconnect 2010 to 2015, 2015 ~ In 2020, the object into the semi-intelligent, intelligent objects into 2020.As the vanguard of the Internet of Things, RFID has become the most concerned about the technology market. The data show that the global RFID market size in 2008 from$ 4.93 billion in 2007 rose to $ 5.29 billion, this figure covers all aspects of the RFID market, including tags, readers and other infrastructure, software and services. RFID card and card-related infrastructure will account for 57.3 percent of the market, reaching $ 3.03 billion. Application from financial and security industries will drive the market growth of RFID cards. Analysys International forecasts, the Chinese RFID market size in 2009 will reach 5.0 billion, a CAGR of 33%, in which the electronic tag is more than 3.8 billion yuan, the reader close to 700 million yuan, software and services market to reach 500 million yuan pattern.MEMS is the abbreviation of the micro-electromechanical systems, MEMS technology is built on the basis of micro / nano, the market prospect is broad. The main advantage of the MEMS sensor is the small size, large-scale mass production cost reduction, mainly used in two major areas of automoti ve and consumer electronics. Under ICInsight the latest report is expected in 2007-2012, global sales of semiconductor sensors and actuators based on MEMS will reach 19 percent compound annual growth rate (CAGR), compared with $ 4.1 billion in 2007 to five years will achieve $ 9.7 billion in annual sales. 7．PrincipleInternet of Things is on the basis of the computer Internet, RFID, wireless data communications technology, to construct a cover everything in the world's "Internet of Things". In this network, the goods (products) to each other "exchange", without the need for human intervention. Its essence is the use of radio frequency identification (RFID) technology to achieve the interconnection and sharing of the automatic identification of goods (products) and information through the computer Internet.The Internet of Things is a very important technology is radio frequency identification (RFID) technology. RFID is radio frequency identification (Radio Frequency Identification) technology abbreviation, is an automatic identification technology in the 1990s began to rise, the more advanced a non-contact identification technology. The development of RFID technology based on a simple RFID system, combined with existing network technology, database technology, middleware technology, to build a one composed by a large number of networked readers and numerous mobile label, much larger than the Internet of Things trend.RFID, It is able to let items "speak" a technique. In the "Internet of Things" concept, RFID tags are stored in the specification and interoperability information collected automatically by wireless data communications network to a central information system, to achieve the identification of goods (products), and then through the open computer network for information exchange and sharing, items "transparent" management.The information technology revolution in the Internet of Things is referred to as IT mobile Pan of a specific application. Internet of Things through IntelliSense, identification technology and pervasive computing, ubiquitous network convergence applications, breaking the conventional thinking before, human beings can achieve ubiquitous computing and network connectivity [3]. The traditional thinking has been the separation of physical infrastructure and IT infrastructure: on the one hand, airports, roads, buildings, while on the other hand, the data center, PC, broadband. In the era of the "Internet of Things", reinforced concrete, cable with the chip, broadband integration into a unified infrastructure, in this sense, the infrastructure is more like a new site of the Earth, the world really works it, which including economic management, production operation, social and even personal life. "Internet of Things" makes it much more refined and dynamic management of production and life, to manage the future of the city to achieve the status of "wisdom" to improve resource utilization and productivity levels, and improve the relationship between man and nature.8．Agency1, institution-buildingAs the first national Internet of Things industry community organizations - the application of professional Committee of China Electronic Chamber of Things technology products (referred to as: "objects of the IPCC"), the Ministry of Civil Affairs in June 2010, preliminary approved by the Ministry of August being reported that the Ministry of Civil Affairs for final approval.2, the main taskServe as a bridge between business and government to assist the Government of the industry guidance, coordination, consultation and services to help members to reflect the business requirements to the Government; coordinate the relationship between enterprisesto strengthen technical cooperation, product distribution, the elimination of vicious competition ; supervision of members the correct implementation of national laws and regulations, to regulate the industry; member of information communication technology products, cooperation, resource sharing, capital operation, and promote the app lication of Internet of Things technologies and products, and promote the Internet of Things industrial scale , co-development.9．ConstructionInternet of Things in the practical application to carry out requires the involvement of all walks of life, and need the guidance of the national government as well as related regulations and policies to assist the launching of the Internet of Things has the scale, broad participation, management, technical, and material properties, etc. other features, the technical problem is the most crucial issues of Things billion Bo logistics consulting, Internet of Things technology is an integrated technology, a system not yet which company has overall responsibility for network planning and construction of the entire system, theoretical studies have commenced in all walks of life and the practical application is limited to within the industry. The key is on the planning and design and research and development of the Internet of Things research in the field of RFID, sensors, embedded software, and transmission of data calculation. In general, to carry out the steps of the Internet of things mainly as follows:(1) identified the object attributes, properties, including static and dynamic properties of the static property can be stored directly in the label, the dynamic properties need to start with sensors to detect real-time;(2) the need to identify the equipment to complete the reading of object attributes, and information into a data format suitable for network transmission;(3) the object of information transmitted over the network to the information processing center (processing center may be distributed, such as home computers or mobile phones, may also be centralized, such as China Mobile IDC) by the processing center to complete the object communication calculation.10．key areasInternet of Things 4 key areas:(1) RFID;(2) sensor network;(3) The M2M;(4) integration of the two.11.TrendIndustry experts believe that the Internet of things on the one hand can improve economic efficiency and significant cost savings; the other hand, can provide technical impetus to global economic recovery. Currently, the United States, the European Union are all invested heavily in-depth study to explore the Internet of Things. The country is also highly concerned about the emphasis of Things, Industry and Information T echnology Ministry in conjunction with the relevant departments are conducting research in a new generation of IT to the formation of policies and measures to support the development of a new generation of IT.China Mobile CEO Wang Jianzhou has repeatedly mentioned the Internet of Things will become the focus of future development of China Mobile. He will be invited to T aiwan to produce RFID, sensors and bar code manufacturers and China Mobile. According to him, the use of the Internet of Things technology, Shanghai Mobile has a number of industrial customers tailor the data collection, transmission, processing and business management in one set of wireless application solutions. The latest data show that Shanghai Mobile has more than 100,000 chips mounted on a taxi, bus, various forms of matter networking applications in all walks of prowess, to ensure the orderly operation of the city. During the Shanghai World Expo, "the bus services through" will be fully applied to the Shanghai public transport system, the smooth flow traffic to the most advanced technology to protect Expo area; for logistics transportation management, e-logistics ", will provide users with real-time accurate information of Cargo, vehicle tracking and positioning, the transport path selection, logistics network design and optimization services greatly enhance the comprehensive competitiveness of logistics enterprises.In addition, the popularization of the "Internet of Things" for the number of animals, plants and machinery, sensors and RFID tags of items and related interface devices will greatly exceed the number of mobile phones. The promotion of the Internet of Things willbecome a drive to promote economic development for the industry to open up a potential development opportunities. According to the current demand on the Internet of Things, in recent years, billions of sensors and electronic tags, which will greatly promote the production of IT components, while increasing the number of job opportunities.According to reports, it is necessary to truly build an effective Internet of things, there are two important factors. First, the scale, only with the scale to make the items of intelligence play a role. For example, a city of one million vehicles, if we only 10000 vehicles installed on the smart system, it is impossible to form an intelligent transportation system; two mobility items are usually not static, but in the state of the movement , we must maintain the items in the state of motion, and even high-speed motion state can at any time for dialogue.FORRESTER of the authority of the U.S. advisory body predicted that 2020, the world of business of the Internet of Things, compared with the business of interpersonal communication, will reach 30 to 1, so the "Internet of Things" is known to be the next one trillion communications services.Internet of Things heat wave Why is rapidly growing in China? Internet of Things in China rapid rise thanks to the several advantages of our country in terms of things.In the early 1999 launched the Internet of Things core sensor network technology research, R & D level in the world; the second, sensor network field in the world, China is the standard one of the dominant country, the patent owner; third China is one of the countries to achieve a complete industrial chain of Things; Fourth, China's wireless communications network and broadband coverage provides a solid infrastructure to support the development of the Internet of Things; Fifth, China has become the world's first the three major economies, with strong economic strength to support the development of the Internet of Things.12.MythThe current understanding of the Internet of things there are a lot of misunderstanding, which is also a direct impact on our understanding of Things on the development of the logistics industry, it is necessary first to distinguish errors, clarify our thinking.One sensor networks or RFID network equivalent of Things. The fact that sensortechnology, or RFID technology, or are simply one of the information collection technology. In addition to the sensor technology and RFID technology, GPS, video recognition, infrared, laser, scanning can be achieved automatically identify physical objects to communicate technical information collection technology can become the Internet of Things. Sensor networks or RFID network is just an application of Things, but not all of Things.Second, the Internet of Things as a myriad of unlimited extension of the Internet of Things as a completely open for all things, all of the interconnections, all shared Internet platform.In fact, the Internet of Things is not simple infinite extension of the global sharing of the Internet. Even if the Internet is also not only refers to we typically think of the international sharing computer network, Internet, WAN and LAN. Internet of Things can be both an extension of our usual sense of the Internet to the matter; LAN, professional can also be based on real needs and industrial applications. The reality is not necessary and can not make all the items networking; no need to make professional, LAN must be connected to the global Internet sharing platform. Of things in the future the Internet will be very different from the professional network of similar smart logistics, smart transportation, smart grid; the intelligence community and other local area network is the largest use of space.T er, that the ubiquitous network of the Internet of Things Internet of Things, and therefore the Internet of Things is a castle in the air, is difficult to achieve the technology. In fact the Internet of things are real, many of the primary Internet of Things applications already for our services. The Internet of Things concept is introduced in many real-world applications based on polymeric integrated innovation, pre-existing network with the Internet of Things, intelligent, automated system, summarized and upgrading it upgraded from a higher perspective our knowledge.Four of Things as a basket, and everything installed inside; based on self-awareness, and only be able to interact, communication products as the Internet of Things applications. For example, just embedded some of the sensors, to become the so-called Internet of Things appliances; products labeled with RFID tags, became the Internet of Things applications.es。

物联网工程中英文对照外文翻译文献

中英文对照外文翻译(文档含英文原文和中文翻译)Android: A Programmer’s Guide1 What Is Android1.1 Key Skills & Concepts● History of embedded device programming● Explanation of Open Handset Alliance● First look at the Android home screenIt can be said that, for a while, traditional desktop application developers have been spoiled. This is not to say that traditional desktop application development is easier than other forms of develop ment. However, as traditional desktop application developers, we have had the ability to create alm ost any kind of application we can imagine. I am including myself in this grouping because I got my start in desktop programming.One aspect that has made desktop programming more accessible is that we have had the ability to interact with the desktop operating system, and thus interact with any underlying hardware, pretty1freely (or at least with minimal exceptions). This kind of freedom to program independently, how ever, has never really been available to the small group of programmers who dared to venture int o the murky waters of cell phone development.NOTE ：I refer to two different kinds of developers in this discussion: traditional desktop applicati on developers, who work in almost any language and whose end product, applications, are built to run on any “desktop” operating system; and Android developers, J ava developers who develop for the Android platform. This is not for the purposes of saying one is by any means better or wors e than the other. Rather, the distinction is made for purposes of comparing the development styles and tools of desktop operating system environments to the mobile operating system environment1.2 Brief History of Embedded Device ProgrammingFor a long time, cell phone developers comprised a small sect of a slightly larger group of developers known as embedded device developers. Seen as a less “glamorous” sibling to desktop—and later web—development, embedded device development typically got the proverbial short end of the stick as far as hardware and operating system features, because embedded device manufacturers were notoriously stingy on feature support.Embedded device manufacturers typically needed to guard their hardware secrets closely, so they gave embedded device developers few libraries to call when trying to interact with a specific device. Embedded devices differ fro m desktops in that an embedded device is typically a “computer on a chip.” For example, consider your standard television remote control; it is not really seen as an overwhelming achievement of technological complexity. When any button is pressed, a chip interprets the signal in a way that has been programmed into the device. This allows the device to know what to expect from the input device (key pad), and how to respond to those commands (for example, turn on the television). This is a simple form of embedded device programming. However, believe it or not, simple devices such as these are definitely related to the roots of early cell phone devices and development.Most embedded devices ran (and in some cases still run) proprietary operating systems. The reason for choosing to create a proprietary operating system rather than use any consumer system was really a product of necessity. Simple devices did not need very robust and optimized operating systems.As a product of device evolution, many of the more complex embedded devices, such as early PDAs, household security systems, and GPSs, moved to somewhat standardized operating system platforms about five years ago. Small-footprint operating systems such as Linux, or even an embedded version of Microsoft Windows, have become more prevalent on many embedded devices. Around this time in device evolution, cell phones branched from other embedded devices onto their own path. This branching is evident whenyou examine their architecture.Nearly since their inception, cell phones have been fringe devices insofar as they run on proprietary software—software that is owned and controlled by the manufacturer, and is almost always considered to be a “closed” system. The practice of manufacturers using proprietary operating systems began more out of necessity than any other reason. That is, cell phone manufacturers typically used hardware that was completely developed in-house, or at least hardware that was specifically developed for the purposes of running cell phone equipment. As a result, there were no openly available, off-the-shelf software packages or solutions that would reliably interact with their hardware. Since the manufacturers also wanted to guard very closely their hardware trade secrets, some of which could be revealed by allowing access to the software level of the device, the common practice was, and in most cases still is, to use completely proprietary and closed software to run their devices. The downside to this is that anyone who wanted to develop applications for cell phones needed to have intimate knowledge of the proprietary environment within which it was to run. The solution was to purchase expensive development tools directly from the manufacturer. This isolated many of the “homebrew” develo pers.NOTE：A growing culture of homebrew developers has embraced cell phone application development. The term “homebrew” refers to the fact that these developers typically do not work for a cell phone development company and generally produce small, one-off products on their own time.Another, more compelling “necessity” that kept cell phone development out of the hands of theeveryday developer was the hardware manufacturers’ solution to the “memory versus need” dilemma. Until recently, cell phones did little more than execute and receive phone calls, track your contacts, and possiblysend and receive short text messages; not really the “Swiss army knives” of technology they are today.Even as late as 2002, cell phones with cameras were not commonly found in the hands of consumers.By 1997, small applications such as calculators and games (Tetris, for example) crept their way ontocell phones, but the overwhelming function was still that of a phone dialer itself. Cell phones had not yetbecome the multiuse, multifunction personal tools they are today. No one yet saw the need for Internetbrowsing, MP3 playing, or any of the multitudes of functions we are accustomed to using today. It ispossible that the cell phone manufacturers of 1997 did not fully perceive the need consumers would havefor an all-in-one device. However, even if the need was present, a lack of device memory and storagecapacity was an even bigger obstacle to overcome. More people may have wanted their devices to be all-in-one tools, but manufacturers still had to climb the memory hurdle.To put the problem simply, it takes memory to store and run applications on any device, cell phones included. Cell phones, as a device, until recently did not have the amount of memory available to them thatwould facilitate the inclusion of “extra” programs. Within the last two years, the price of memory hasreached very low levels. Device manufacturers now have the ability to include more memory at lowerprices. Many cell phones now have more standard memory than the average PC had in the mid-1990s. So,now that we have the need, and the memory, we can all jump in and develop cool applications for cellphones around the world, right? Not exactly.Device manufacturers still closely guard the operating systems that run on their devices. While a fewhave opened up to the point where they will allow some Java-based applications to run within a smallenvironment on the phone, many do not allow this. Even the systems that do allow some Java apps to rundo not allow the kind of access to the “core” system that standard desktop developers are accustomed to having.1.3 Open Handset Alliance and AndroidThis barrier to application development began to crumble in November of 2007 when Google, under theOpen Handset Alliance, released Android. The Open Handset Alliance is a group of hardware and softwaredevelopers, including Google, NTT DoCoMo, Sprint Nextel, and HTC, whose goal is to create a more opencell phone environment. The first product to be released under the alliance is the mobile device operatingsystem, Android.With the release of Android, Google made available a host of development tools and tutorials to aid would-be developers onto the new system. Help files, the platform software development kit (SDK), and even a developers’ community can be found at Google’s Android website, This site should be your starting point, and I highly encourage you to visit the site.NOTE ：Google, in promoting the new Android operating system, even went as far as to create a $10million contest looking for new and exciting Android applications.While cell phones running Linux, Windows, and even PalmOS are easy to find, as of this writing, nohardware platforms have been announced for Android to run on. HTC, LG Electronics, Motorola, andSamsung are members of the Open Handset Alliance, under which Android has been released, so we canonly hope that they have plans for a few Android-based devices in the near future. With its release inNovember 2007, the system itself is still in a software-only beta. This is good news for developers because it gives us a rare advance look at a future system and a chance to begin developing applications that willrun as soon as the hardware is released.NOTE：This strategy clearly gives the Open Handset Alliance a big advantage over other cell phone operating system developers, because there could be an uncountable number of applications available immediately for the first devices released to run Android.Introduction to AndroidAndroid, as a system, is a Java-based operating system that runs on the Linux 2.6 kernel. The system is very lightweight and full featured. Android applications are developed using Java and can be ported rather easily to the new platform. If you have not yet downloaded Java or are unsure about which version you need, I detail the installation of the development environment in Chapter 2. Other features of Android include an accelerated 3-D graphics engine (based on hardware support), database support powered by SQLite, and an integrated web browser.If you are familiar with Java programming or are an OOP developer of any sort, you are likely used to programmatic user interface (UI) development—that is, UI placement which is handled directly within the program code. Android, while recognizing and allowing for programmatic UI development, also supports the newer, XML-based UI layout. XML UI layout is a fairly new concept to the average desktop developer. I will cover both the XML UI layout and the programmatic UI development in the supporting chapters of this book.One of the more exciting and compelling features of Android is that, because of its architecture, third-partyapplications—including those that are “home grown”—are executed with the same system priority as those that are bundled with the core system. This is a major departure from most systems, which give embeddedsystem apps a greater execution priority than the thread priority available to apps created by third-partydevelopers. Also, each application is executed within its own thread using a very lightweight virtualmachine.Aside from the very generous SDK and the well-formed libraries that are available to us to develop with,the most exciting feature for Android developers is that we now have access to anything the operatingsystem has access to. In other words, if you want to create an application that dials the phone, you haveaccess to the phone’s dialer; if you want to create an application that utilizes the phone’s internal GPS (ifequipped), you have access to it. The potential for developers to create dynamic and intriguing applicationsis now wide open.On top of all the features that are available from the Android side of the equation, Google has thrown insome very tantalizing features of its own. Developers of Android applications will be able to tie their applications into existing Google offerings such as Google Maps and the omnipresent Google Search.Suppose you want to write an application that pulls up a Google map of where an incoming call isemanating from, or you want to be able to store common search results with your contacts; the doors ofpossibility have been flung wide open with Android.Chapter 2 begins your journey to Android development. You will learn the how’s and why’s of usingspecific development environments or integrated development environments (IDE), and you will downloadand install the Java IDE Eclipse.2 Application: Hello World2.1 Key Skills & Concepts● Creating new Android projects● Working with Views● Using a TextView● Modifying the main.xml file● Running applications on the Android EmulatorIn this chapter, you will be creating your first Android Activity. This chapter examines theapplication-building process from start to finish. I will show you how to create an Android project inEclipse, add code to the initial files, and run the finished application in the Android Emulator. The resultingapplication will be a fully functioning program running in an Android environment.Actually, as you move through this chapter, you will be creating more than one Android Activity.Computer programming tradition dictates that your first application be the typical Hello World! application,so in the first section you will create a standard Hello World! application with just a blank background andthe “Hello World!” text. Then, for the sake of enabling you to get to know the language better, the next section explains in detail the files automatically created by Android for your Hello World! application. You will create two iterations of this Activity, each using different techniques for displaying information to the screen. You will also create two different versions of a Hello World! application that will display an image that delivers the “Hello World!” message. This will give you a good introduction to the controls and inner workings of Android.NOTE：You will often see “application” and “Activity” used interchangeably. The difference between the two is that an application can be composed of multiple Activities, but one application must have at leastone Activity. Each “window” or screen of your application is a separate Activity. Therefore, if you create a fairly simple application with only one screen of data (like the Hello World! application in this chapter),that will be one Activity. In future chapters you will create applications with multiple Activities.To make sure that you get a good overall look at programming in Android, in Chapter 6 you will createboth of these applications in the Android SDK command-line environment for Microsoft Windows andLinux. In other words, this chapter covers the creation process in Eclipse, and Chapter 6 covers the creationprocess using the command-line tools. Therefore, before continuing, you should check that your Eclipseenvironment is correctly configured. Review the steps in Chapter 3 for setting the PATH statement for theAndroid SDK. You should also ensure that the JRE is correctly in your PATH statement.TIP：If you have configuration-related issues while attempting to work with any of the command-lineexamples, try referring to the configuration steps in Chapters 2 and 3; and look at the Android SDK documentation.2.2 Creating Your First Android Project in EclipseTo start your first Android project, open Eclipse. When you open Eclipse for the first time, it opens toan empty development environment (see Figure 5-1), which is where you want to begin. Your first task isto set up and name the workspace for your application. Choose File | New | Android Project, which willlaunch the New Android Project wizard.CAUTION Do not select Java Project from the New menu. While Android applications are written in Java, and you are doing all of your development in Java projects, this option will create a standard Java application. Selecting Android Project enables you to create Android-specific applications.If you do not see the option for Android Project, this indicates that the Android plugin for Eclipse was not fully or correctly installed. Review the procedure in Chapter 3 for installing the Android plugin for Eclipse to correct this.2.3 The New Android Project wizard creates two things for youA shell application that ties into the Android SDK, using the android.jar file, and ties the project intothe Android Emulator. This allows you to code using all of the Android libraries and packages, and alsolets you debug your applications in the proper environment.Your first shell files for the new project. These shell files contain some of the vital application blocksupon which you will be building your programs. In much the same way as creating a Microsoft .NETapplication in Visual Studio generates some Windows-created program code in your files, using the Android Project wizard in Eclipse generates your initial program files and some Android-created code. Inaddition, the New Android Project wizard contains a few options, shown next, that you must set to initiate your Android project. For the Project Name field, for purposes of this example, use the titleHelloWorldText. This name sufficiently distinguishes this Hello World! project from the others that youwill be creating in this chapter.In the Contents area, keep the default selections: the Create New Project inWorkspace radio button should be selected and the Use Default Location check box should be checked.This will allow Eclipse to create your project in your default workspace directory. The advantage ofkeeping the default options is that your projects are kept in a central location, which makes ordering,managing, and finding these projects quite easy. For example, if you are working in a Unix-basedenvironment, this path points to your $HOME directory.If you are working in a Microsoft Windows environment, the workspace path will beC:/Users/<username>/workspace, as shown in the previous illustration. However, for any number of reasons, you may want to uncheck the Use Default Location check box and select a different location for your project. One reason you may want to specify a different location here is simply if you want to choose a location for this specific project that is separate from other Android projects. For example, you may want to keep the projects that you create in this book in a different location from projects that you create in the future on your own. If so, simply override the Location option to specify your own custom location directory for this project.3 Application FundamentalsAndroid applications are written in the Java programming language. The compiled Java code — along with any data and resource files required by the application — is bundled by the aapt tool into an Androidpackage, an archive file marked by an .apk suffix. This file is the vehicle for distributing the application and installing it on mobile devices; it's the file users download to their devices. All the code in a single .apk file is considered to be one application.In many ways, each Android application lives in its own world:1. By default, every application runs in its own Linux process. Android starts the process when any of the application's code needs to be executed, and shuts down the process when it's no longer needed and system resources are required by other applications.2. Each process has its own virtual machine (VM), so application code runs in isolation from the code of all other applications.3. By default, each application is assigned a unique Linux user ID. Permissions are set so that the application's files are visible only to that user and only to the application itself — although there are ways to export them to other applications as well.It's possible to arrange for two applications to share the same user ID, in which case they will be able to see each other's files. To conserve system resources, applications with the same ID can also arrange to run in the same Linux process, sharing the same VM.3.1 Application ComponentsA central feature of Android is that one application can make use of elements of other applications (provided those applications permit it). For example, if your application needs to display a scrolling list of images and another application has developed a suitable scroller and made it available to others, you can call upon that scroller to do the work, rather than develop your own. Application have four types of components:(1)ActivitiesAn activity presents a visual user interface for one focused endeavor the user can undertake. For example, an activity might present a list of menu items users can choose from or it might display photographs along with their captions. A text messaging application might have one activity that shows a list of contacts to send messages to, a second activity to write the message to the chosen contact, and other activities to review old messages or change settings. Though they work together to form a cohesive user interface, each activity is independent of the others. Each one is implemented as a subclass of the Activity base class.An application might consist of just one activity or, like the text messaging application just mentioned, it may contain several. What the activities are, and how many there are depends, of course, on the application and its design. Typically, one of the activities is marked as the first one that should be presented to the user when the application is launched. Moving from one activity to another is accomplished by having the current activity start the next one.Each activity is given a default window to draw in. Typically, the window fills the screen, but it might be smaller than the screen and float on top of other windows. An activity can also make use of additional windows —— for example, a pop-up dialog that calls for a user response in the midst of the activity, or a windowswindow that presents users with vital information when they select a particular item on-screen.The visual content of the window is provided by a hierarchy of views — objects derived from the base View class. Each view controls a particular rectangular space within the window. Parent views contain and organize the layout of their children. Leaf views (those at the bottom of the hierarchy) draw in the rectangles they control and respond to user actions directed at that space. Thus, views are where the activity's interaction with the user takes place.For example, a view might display a small image and initiate an action when the user taps that image. Android has a number of ready-made views that you can use — including buttons, text fields, scroll bars, menu items, check boxes, and more.A view hierarchy is placed within an activity's window by the Activity.setContentView() method. The content view is the View object at the root of the hierarchy. (See the separate User Interface document for more information on views and the hierarchy.)(2)ServicesA service doesn't have a visual user interface, but rather runs in the background for an indefinite period of time. For example, a service might play background music as the user attends to other matters, or it might fetch data over the network or calculate something and provide the result to activities that need it. Each service extends the Service base class.A prime example is a media player playing songs from a play list. The player application would probably have one or more activities that allow the user to choose songs and start playing them. However, the musicplayback itself would not be handled by an activity because users will expect the music to keep playing even after they leave the player and begin something different. To keep the music going, the media player activity could start a service to run in the background. The system would then keep the music playback service running even after the activity that started it leaves the screen.It's possible to connect to (bind to) an ongoing service (and start the service if it's not already running). While connected, you can communicate with the service through an interface that the service exposes. For the music service, this interface might allow users to pause, rewind, stop, and restart the playback.Like activities and the other components, services run in the main thread of the application process. So that they won't block other components or the user interface, they often spawn another thread for time-consuming tasks (like music playback). See Processes and Threads, later.(3)Broadcast receiversA broadcast receiver is a component that does nothing but receive and react to broadcast announcements. Many broadcasts originate in system code — for example, announcements that the timezone has changed, that the battery is low, that a picture has been taken, or that the user changed a language preference. Applications can also initiate broadcasts — for example, to let other applications know that some data has been downloaded to the device and is available for them to use.An application can have any number of broadcast receivers to respond to any announcements it considers important. All receivers extend the BroadcastReceiver base class.Broadcast receivers do not display a user interface. However, they may start an activity in response to the information they receive, or they may use the NotificationManager to alert the user. Notifications can get the user's attention in various ways —— flashing the backlight, vibrating the device, playing a sound, and so the user's attention in various wayson. They typically place a persistent icon in the status bar, which users can open to get the message.(4)Content providersA content provider makes a specific set of the application's data available to other applications. The data can be stored in the file system, in an SQLite database, or in any other manner that makes sense. The content provider extends the ContentProvider base class to implement a standard set of methods that enable other applications to retrieve and store data of the type it controls. However, applications do not call these methods directly. Rather they use a ContentResolver object and call its methods instead. A ContentResolver can talk to any content provider; it cooperates with the provider to manage any interprocess communication that's involved.See the separate Content Providers document for more information on using content providers. Whenever there's a request that should be handled by a particular component, Android makes sure that the application process of the component is running, starting it if necessary, and that an appropriate instance of the component is available, creating the instance if necessary.3.2 Activating components: intentsContent providers are activated when they're targeted by a request from a ContentResolver. The other three components — activities, services, and broadcast receivers — are activated by asynchronous messages called intents. An intent is an Intent object that holds the content of the message. For activities and services, it names the action being requested and specifies the URI of the data to act on, among other things. For example, it might convey a request for an activity to present an image to the user or let the user edit some text. For broadcast receivers, theIntent object names the action being announced. For example, it might announce to interested parties that the camera button has been pressed.。

Hadoop云计算外文翻译文献

Hadoop云计算外文翻译文献(文档含中英文对照即英文原文和中文翻译)原文:Meet HadoopIn pioneer days they used oxen for heavy pulling, and when one ox couldn’t budge a log, they didn’t try to grow a larger ox. We shouldn’t be trying for bigger computers, but for more systems of computers.—Grace Hopper Data!We live in the data age. It’s not easy to measure the total volume of data stored electronically, but an IDC estimate put the size of the “digital universe” at 0.18 zettabytes in2006, and is forecasting a tenfold growth by 2011 to 1.8 zettabytes. A zettabyte is 1021 bytes, or equivalently one thousand exabytes, one million petabytes, or one billion terabytes. That’s roughly the same order of magnitude as one disk drive for every person in the world.This flood of data is coming from many sources. Consider the following:• The New York Stock Exchange generates about one terabyte of new trade data perday.• Facebook hosts approximately 10 billion photos, taking up one petabyte of storage.• , the genealogy site, stores around 2.5 petabytes of data.• The Internet Archive stores around 2 petabytes of data, and is growing at a rate of20 terabytes per month.• The Large Hadron Collider near Geneva, Switzerland, will produce about 15 petabytes of data per year.So there’s a lot of data out there. But you are probably wondering how it affects you.Most of the data is locked up in the largest web properties (like search engines), orscientific or financial institutions, isn’t it? Does the advent of “Big Data,” as it is being called, affect smaller organizations or individuals?I argue that it does. Take photos, for example. My wife’s grandfather was an avid photographer, and took photographs throughout his adult life. His entire corpus of medium format, slide, and 35mm film, when scanned in at high-resolution, occupies around 10 gigabytes. Compare this to the digital photos that my family took last year,which take up about 5 gigabytes of space. My family is producing photographic data at 35 times the rate my wife’s grandfather’s did, and the rate is increasing every year as it becomes easier to take more and more photos.More generally, the digital streams that individuals are producing are growing apace. Microsoft Research’s MyLifeBits project gives a glimpse of archiving of pe rsonal information that may become commonplace in the near future. MyLifeBits was an experiment where an individual’s interactions—phone calls, emails, documents were captured electronically and stored for later access. The data gathered included a photo taken every minute, which resulted in an overall data volume of one gigabyte a month. When storage costs come down enough to make it feasible to store continuous audio and video, the data volume for a future MyLifeBits service will be many times that.The t rend is for every individual’s data footprint to grow, but perhaps more importantly the amount of data generated by machines will be even greater than that generated by people. Machine logs, RFID readers, sensor networks, vehicle GPS traces, retail transactions—all of these contribute to the growing mountain of data.The volume of data being made publicly available increases every year too. Organizations no longer have to merely manage their own data: success in the future will be dictated to a large extent by their ability to extract value from other organizations’ data.Initiatives such as Public Data Sets on Amazon Web Services, , and exist to foster the “information commons,” where data can be freely (or in the case of AWS, for a modest price) shared for anyone to download and analyze. Mashups between different information sources make for unexpected and hitherto unimaginable applications.Take, for example, the project, which watches the Astrometry groupon Flickr for new photos of the night sky. It analyzes each image, and identifies which part of the sky it is from, and any interesting celestial bodies, such as stars or galaxies. Although it’s still a new and experimental service, it shows the kind of things that are possible when data (in this case, tagged photographic images) is made available andused for something (image analysis) that was not anticipated by the creator.It has been said that “More data usually beats better algorithms,” which is to say that for some problems (such as recommending movies or music based on past preferences),however fiendish your algorithms are, they can often be beaten simply by having more data (and a less sophisticated algorithm).The good news is that Big Data is here. The bad news is that we are struggling to store and analyze it.Data Storage and AnalysisThe problem is simple: while the storage capacities of hard drives have increased massively over the years, access speeds--the rate at which data can be read from drives--have not kept up. One typical drive from 1990 could store 1370 MB of data and had a transfer speed of 4.4 MB/s, so you could read all the data from a full drive in around five minutes. Almost 20years later one terabyte drives are the norm, but the transfer speed is around 100 MB/s, so it takes more than two and a half hours to read all the data off the disk.This is a long time to read all data on a single drive and writing is even slower. The obvious way to reduce the time is to read from multiple disks at once. Imagine if we had 100 drives, each holding one hundredth of the data. Working in parallel, we could read the data in under two minutes.Only using one hundredth of a disk may seem wasteful. But we can store one hundred datasets, each of which is one terabyte, and provide shared access to them. We can imagine that the users of such a system would be happy to share access in return for shorter analysis times, and, statistically, that their analysis jobs would be likely to be spread over time, so they wouldn`t interfere with each other too much.There`s more to being able to read and write data in parallel to or from multiple disks, though. The first problem to solve is hardware failure: as soon as you start using many pieces of hardware, the chance that one will fail is fairly high. A common way of avoiding data loss is through replication: redundant copies of the data are kept by the system so that in the event of failure, there is another copy available. This is how RAID works, for instance, although Hadoop`s filesystem, the Hadoop Distributed Filesystem (HDFS),takes a slightly different approach, as you shall see later. The second problem is that most analysis tasks need to be able to combine the data in some way; data read from one disk may need to be combined with the data from any of the other 99 disks. Various distributed systems allow data to be combined from multiple sources, but doing this correctly is notoriously challenging. MapReduce provides a programming model that abstracts the problem from disk reads and writes, transforming it into a computation over sets of keys and values. We will look at the details of this model in later chapters, but the important point for the present discussion is that there are two parts to the computation, the map a nd the reduce, and it’s the interface between the two where the “mixing” occurs. Like HDFS, MapReduce has reliability built-in.This, in a nutshell, is what Hadoop provides: a reliable shared storage and analysis system. The storage is provided by HDFS, and analysis by MapReduce. There are other parts to Hadoop, but these capabilities are its kernel.Comparison with Other SystemsThe approach taken by MapReduce may seem like a brute-force approach. The premise is that the entire dataset—or at least a good portion of it—is processed for each query. But this is its power. MapReduce is a batch query processor, and the ability to run an ad hoc query against your whole dataset and get the results in a reasonable time is transformative. It changes the way you think about data, and unlocks data that was previously archived on tape or disk. It gives people the opportunity to innovate with data. Questions that took too long to get answered before can now be answered, which in turn leads to new questions and new insights.For example, Mailtrust, Rackspace’s mail division, used Hadoop for processing email logs. One ad hoc query they wrote was to find the geographic distribution of their users.In their words: This data was so useful that we’ve scheduled the MapReduce job to run monthly and we will be using this data to help us decide which Rackspace data centers to place new mail servers in as we grow. By bringing several hundred gigabytes of data together and having the tools to analyze it, the Rackspace engineers were able to gain an understanding of the data that they otherwise would never have had, and, furthermore, they were able to use what they had learned to improve the service for their customers. You can read more about how Rackspace uses Hadoop in Chapter 14.RDBMSWhy can’t we use databases with lots of disks to do large-scale batch analysis? Why is MapReduce needed? The answer to these questions comes from another trend in disk drives: seek time is improving more slowly than transfer rate. Seeking is the process of moving the disk’s head to a particular place on the disk to read or write data. It characterizes the latency of a disk operation, whereas the transfer rate corresponds to a disk’s bandwidth.If the data access pattern is dominated by seeks, it will take longer to read or write large portions of the dataset than streaming through it, which operates at the transfer rate. On the other hand, for updating a small proportion of records in a database, a traditional B-Tree (the data structure used in relational databases, which is limited by the rate it can perform seeks) works well. For updating the majority of a database, a B-Tree is less efficient than MapReduce, which uses Sort/Merge to rebuild the database.In many ways, MapReduce can be seen as a complement to an RDBMS. (The differences between the two systems are shown in Table 1-1.) MapReduce is a good fit for problems thatneed to analyze the whole dataset, in a batch fashion, particularly for ad hoc analysis. An RDBMS is good for point queries or updates, where the dataset has been indexed to deliver low-latency retrieval and update times of a relatively small amount of data. MapReduce suits applications where the data is written once, and read many times, whereas a relational database is good for datasets that are continually updated.Table 1-1. RDBMS compared to MapReduceTraditional RDBMS MapReduceData size Gigabytes PetabytesAccess Interactive and batch BatchWrite once, read many times Updates Read and write manytimesStructure Static schema Dynamic schemaIntegrity High LowScaling Nonlinear LinearAnother difference between MapReduce and an RDBMS is the amount of structure in the datasets that they operate on. Structured data is data that is organized into entities that have a defined format, such as XML documents or database tables that conform to a particular predefined schema. This is the realm of the RDBMS. Semi-structured data, on the other hand, is looser, and though there may be a schema, it is often ignored, so it may be used only as a guide to the structure of the data: for example, a spreadsheet, in which the structure is the grid of cells, although the cells themselves may hold anyform of data. Unstructured data does not have any particular internal structure: for example, plain text or image data. MapReduce works well on unstructured or semistructured data, since it is designed to interpret the data at processing time. In other words, the input keys and values for MapReduce are not an intrinsic property of the data, but they are chosen by the person analyzing the data.Relational data is often normalized to retain its integrity, and remove redundancy. Normalization poses problems for MapReduce, since it makes reading a record a nonlocaloperation, and one of the central assumptions that MapReduce makes is that it is possible to perform (high-speed) streaming reads and writes.A web server log is a good example of a set of records that is not normalized (for example, the client hostnames are specified in full each time, even though the same client may appear many times), and this is one reason that logfiles of all kinds are particularly well-suited to analysis with MapReduce.MapReduce is a linearly scalable programming model. The programmer writes two functions—a map function and a reduce function—each of which defines a mapping from one set of key-value pairs to another. These functions are oblivious to the size of the data or the cluster that they are operating on, so they can be used unchanged for a small dataset and for a massive one. More importantly, if you double the size of the input data, a job will run twice as slow. But if you also double the size of the cluster, a job will run as fast as the original one. This is not generally true of SQL queries.Over time, however, the differences between relational databases and MapReduce systems are likely to blur. Both as relational databases start incorporating some of the ideas from MapReduce (such as Aster Data’s and Greenplum’s databases), and, from the other direction, as higher-level query languages built on MapReduce (such as Pig and Hive) make MapReduce systems more approachable to traditional database programmers.Grid ComputingThe High Performance Computing (HPC) and Grid Computing communities have been doing large-scale data processing for years, using such APIs as Message Passing Interface (MPI). Broadly, the approach in HPC is to distribute the work across a cluster of machines, which access a shared filesystem, hosted by a SAN. This works well for predominantly compute-intensive jobs, but becomes a problem when nodes need to access larger data volumes (hundreds of gigabytes, the point at which MapReduce really starts to shine), since the network bandwidth is the bottleneck, and compute nodes become idle.MapReduce tries to colocate the data with the compute node, so data access is fast since it is local. This feature, known as data locality, is at the heart of MapReduce and is the reason for its good performance. Recognizing that network bandwidth is the most precious resource in a data center environment (it is easy to saturate network links by copying data around),MapReduce implementations go to great lengths to preserve it by explicitly modelling network topology. Notice that this arrangement does not preclude high-CPU analyses in MapReduce.MPI gives great control to the programmer, but requires that he or she explicitly handle the mechanics of the data flow, exposed via low-level C routines and constructs, such as sockets, as well as the higher-level algorithm for the analysis. MapReduce operates only at the higher level: the programmer thinks in terms of functions of key and value pairs, and the data flow is implicit.Coordinating the processes in a large-scale distributed computation is a challenge. The hardest aspect is gracefully handling partial failure—when you don’t know if a remote process has failed or not—and still making progress with the overall computation. MapReduce spares the programmer from having to think about failure, since the implementation detects failed map or reduce tasks and reschedules replacements on machines that are healthy. MapReduce is able to do this since it is a shared-nothing architecture, meaning that tasks have no dependence on one other. (This is a slight oversimplification, since the output from mappers is fed to the reducers, but this is under the control of the MapReduce system; in this case, it needs to take more care rerunning a failed reducer than rerunning a failed map, since it has to make sure it can retrieve the necessary map outputs, and if not, regenerate them by running the relevant maps again.) So from the programmer’s point of view, the order in which the tasks run doesn’t matter. By contrast, MPI programs have to explicitly manage their own checkpointing and recovery, which gives more control to the programmer, but makes them more difficult to write.MapReduce might sound like quite a restrictive programming model, and in a sense itis: you are limited to key and value types that are related in specified ways, and mappers and reducers run with very limited coordination between one another (the mappers pass keys and values to reducers). A natural question to ask is: can you do anything useful or nontrivial with it?The answer is yes. MapReduce was invented by engineers at Google as a system for building production search indexes because they found themselves solving the same problem over and over again (and MapReduce was inspired by older ideas from the functional programming, distributed computing, and database communities), but it has since been used for many other applications in many other industries. It is pleasantly surprising to see the range of algorithms that can be expressed in MapReduce, from image analysis, to graph-based problems,to machine learning algorithms. It can’t solve every problem, of course, but it is a general data-processing tool.You can see a sample of some of the applications that Hadoop has been used for in Chapter 14.Volunteer ComputingWhen people first hear about Hadoop and MapReduce, they oft en ask, “How is it different from SETI@home?” SETI, the Search for Extra-Terrestrial Intelligence, runs a project called SETI@home in which volunteers donate CPU time from their otherwise idle computers to analyze radio telescope data for signs of intelligent life outside earth. SETI@home is the most well-known of many volunteer computing projects; others include the Great Internet Mersenne Prime Search (to search for large prime numbers) and Folding@home (to understand protein folding, and how it relates to disease).Volunteer computing projects work by breaking the problem they are trying to solve into chunks called work units, which are sent to computers around the world to be analyzed. For example, a SETI@home work unit is about 0.35 MB of radio telescope data, and takes hours or days to analyze on a typical home computer. When the analysis is completed, the results are sent back to the server, and the client gets another work unit. As a precaution to combat cheating, each work unit is sent to three different machines, and needs at least two results to agree to be accepted.Although SETI@home may be superficially similar to MapReduce (breaking a problem into independent pieces to be worked on in parallel), there are some significant differences. The SETI@home problem is very CPU-intensive, which makes it suitable for running on hundreds of thousands of computers across the world, since the time to transfer the work unit is dwarfed by the time to run the computation on it. Volunteers are donating CPU cycles, not bandwidth.MapReduce is designed to run jobs that last minutes or hours on trusted, dedicated hardware running in a single data center with very high aggregate bandwidth interconnects. By contrast, SETI@home runs a perpetual computation on untrusted machines on the Internet with highly variable connection speeds and no data locality.译文:初识Hadoop古时候，人们用牛来拉重物，当一头牛拉不动一根圆木的时候，他们不曾想过培育个头更大的牛。

云计算研究现状文献综述及外文文献

本文档包括该专题的：外文文献、文献综述文献标题：An exploratory study on factors affecting the adoption of cloud computing by information professionals作者：Aharony, Noa期刊：The Electronic Library, 33(2), 308-328.年份：2015一、外文文献An exploratory study on factors affecting the adoption of cloud computing byinformation professionals(影响云计算采用与否的一个探索性研究)Aharony, NoaPurpose- The purpose of this study explores what factors may influence information professionals to adopt new technologies, such as cloud computing in their organizations. The objectives of this study are as follows: to what extent does the technology acceptance model (TAM) explain information professionals intentions towards cloud computing, and to what extent do personal characteristics, such as cognitive appraisal and openness to experience, explain information professionals intentions to use cloud computing.Design/methodology/approach- The research was conducted in Israel during the second semester of the 2013 academic year and encompassed two groups of information professionals: librarians and information specialists. Researchers used seven questionnaires to gather the following data: personal details, computer competence, attitudes to cloud computing, behavioral intention, openness to experience, cognitive appraisal and self-efficacy. Findings- The current study found that the behavioral intention to use cloud computing was impacted by several of the TAM variables, personal characteristics and computer competence.Originality/value- The study expands the scope of research about the TAM by applying it to information professionals and cloud computing and highlights the importance of individual traits, such as cognitive appraisal, personal innovativeness, openness to experience and computer competence when considering technology acceptance. Further, the current study proposes that if directors of information organizations assume that novel technologies may improve their organizations' functioning, they should be familiar with both the TAM and the issue of individual differences. These factors may help them choose the most appropriate workers.Keywords: Keywords Cloud computing, TAM, Cognitive appraisal, Information professionals, Openness to experienceIntroductionOne of the innovations that information technology (IT) has recently presented is thephenomenon of cloud computing. Cloud computing is the result of advancements in various technologies, including the Internet, hardware, systems management and distributed computing (Buyya et al. , 2011). Armbrust et al. (2009) suggested that cloud computing is a collection of applications using hardware and software systems to deliver services to end users via the Internet. Cloud computing offers a variety of services, such as storage and different modes of use (Leavitt, 2009). Cloud computing enables organizations to deliver support applications and avoid the need to develop their own IT systems (Feuerlicht et al. , 2010).Due to the growth of cloud computing use, the question arises as to what factors may influence information professionals to adopt new technologies, such as cloud computing, in their organizations. Assuming that using new technologies may improve the functioning of information organizations, this study seeks to explore if information professionals, who often work with technology and use it as an important vehicle in their workplace, are familiar with technological innovations and whether they are ready to use them in their workplaces. As the phenomenon of cloud computing is relatively new, there are not many surveys that focus on it and, furthermore, no one has so far focussed on the attitudes of information professionals towards cloud computing. The research may contribute to an understanding of the variables that influence attitudes towards cloud computing and may lead to further inquiry in this field.The current study uses the well-known technology acceptance model (TAM), a theory for explaining individuals' behaviours towards technology (Davis, 1989; Venkatesh, 2000), as well as personal characteristics, such as cognitive appraisal and openness to new experiences, as theoretical bases from which we can predict factors which may influence information professionals adopting cloud computing in their workplaces. The objectives of this study are to learn the following: the extent to which the TAM explains information professionals' attitudes towards cloud computing, and the extent to which personal characteristics, such as cognitive appraisal and openness to experiences, explain the intention of information professionals to use cloud computing.Theoretical backgroundCloud computingResearchers have divided cloud computing into three layers: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). SaaS has changed the concept of software as a product to that of a service instead. The software runs in the cloud and the user can access it via the Internet to work on an application. PaaS enables powerful tools for developers to create the applications, without having to deal with concerns about the infrastructure. IaaS provides complete infrastructure resources (e.g. servers, software, network equipment and storage). With IaaS, consumers do not have to purchase the latest technology, perform maintenance, upgrade software or buy software licenses (Anuar et al. , 2013). Cloud computing deployment can be divided into four types: private clouds, public clouds, community clouds and hybrid clouds (Mell and Grance, 2011). Public clouds have open access, private clouds run within organizations, community clouds containresources that are shared with others in the community and hybridclouds encompass two or more cloud models. Anuar et al. (2013) presented the main characteristics of cloud computing: flexible scale that enables flexible-scale capabilities for computing; virtualization that offers a new way of getting computing resources remotely, regardless of the location of the user or the resources; high trust , as the cloud offers more reliability to end users than relying on local resources; versatility , because cloud services can serve different sectors in various disciplines use the same cloud; and on demand service , as end users can tailor their service needs and pay accordingly.As cloud computing is relatively new, there are not a lot of surveys that focus on it. Several researchers conducted in-depth interviews investigating respondents' attitudes towards keeping their virtual possessions in the online world (Odom et al. , 2012). Teneyuca (2011) reported on a survey of cloud computing usage trends that included IT professionals as respondents. Results revealed preferences for virtualization and cloud computing technologies. However, the major reasons for cloud computing adoption being impeded were the lack of cloud computing training (43 per cent) and security concerns (36 per cent). Another report showed that nearly 40 per cent of Americans think that saving data to their hard drive is more secure than saving it to a cloud (Teneyuca, 2011). A further study (Ion et al., 2011) explored private users' privacy attitudes and beliefs about cloud computing in comparison with those in companies. Anuar et al. (2013) investigated cloud computing in an academic institution, claiming that cloud computing technology enhances performance within the academic institution. A study that was carried out in the education arena examined factors that led students to adopt cloud computing technology (Behrend et al. , 2010). Technology acceptance modelThe TAM (Davis, 1989) is a socio-technical model which aims to explain user acceptance of an information system. It is based on the theory of reasoned action (TRA) (Fishbein and Ajzen, 1975) which seeks to understand how people construct behaviours. The model suggests that technology acceptance can be explained according to the individual's beliefs, attitudes and intentions (Davis, 1989). The TAM hypothesizes that one's intention is the best predictor of usage behaviour and suggests that an individual's behavioural intention to use technology is determined by two beliefs: perceived usefulness (PU) and perceived ease of use (PEOU). PU refers to the individual's perception that using a technology will improve performance and PEOU addresses a user's perceptions that using a particular system would be free of effort (Davis, 1989). The current study concentrates on PEOU as the researchers wanted to examine if information professionals' perceptions about new technology is affected by its simplicity and friendly interface. Earlier research mainly investigated personal behaviour to use new information systems and technology in the following: corporate environments (Gefen and Straub, 1997);Web shopping (Chang et al. , 2002; Lin and Lu, 2000);education, particularly e-learning (Park, 2009) and m-learning (Aharony, 2014); and the library arena (Aharony, 2011; Park et al. , 2009).Personal innovativenessA construct which may contribute to information professionals' intention behaviour to use cloud computing is personal innovativeness, a major characteristic in innovation diffusion research in general (Agarwal and Prasad, 1998; Rogers, 1983, 1995). Agarwal and Prasad (1998) have coined the term "personal innovativeness in the domain of IT" (PIIT), which describes a quite stable characteristic of the individual across situational considerations. Previous studies found that personal innovativeness is a significant determinant of PEOU, as well as of PU (Agarwal and Karahanna, 2000; Lewis et al. , 2003). Several researchers have suggested that innovative people will search for intellectually or sensorially stimulating experiences (Uray and Dedeoglu, 1997).Openness to experienceAnother variable that may predict respondents' perspectives towards cloud computing is openness to experience which addresses the tendency to search for new and challenging experiences, to think creatively and to enjoy intellectual inquiries (McCrae and Sutin, 2009). People who are highly open to experience are perceived as also open to new challenges, thoughts and emotions (McCrae and Costa, 2003). Studies reported that there is a positive relation between openness to experience and intelligence tests (Gignac et al. , 2004). According to Weiss et al. (2012), challenging transitions may influence differently those who are high or low in openness to experience. Those who are high may approach these situations with curiosity, emphasizing the new possibilities offered to them. However, those who are low in openness may be threatened and try to avoid them by adhering to predictable environments. Various researchers note that people who are high in openness to experience are motivated to resolve new situations (McCrae, 1996; Sorrentino and Roney, 1999). Furthermore, openness to experience is associated with cognitive flexibility and open-mindedness (McCrae and Costa, 1997), and negatively associated with rigidity, uncertainty and inflexibility (Hodson and Sorrentino, 1999). Thus, people who are less open to experience tend to avoid novelty and prefer certainty. Studies reveal that openness to experience declines in the later years (Allemand et al. , 2007; Donnellan and Lucas, 2008).Challenge and threatThe following section will focus on the personality characteristics of challenge and threat that might affect information professionals' behavioural intention to use cloud computing. Challenge and threat are the main variables of a unidimensional, bipolar motivational state. They are the result of relative evaluations of situational demands and personal resources that are influenced both by cognitive and affective processes in motivated performance situations (Vick et al. , 2008). According to Lazarus and Folkman (1984), challenge refers to the potential for growth or gain and is characterized by excitement and eagerness, while threat addresses potential harm and is characterized by anxiety, fear and anger. Situations that suggest low demands and high resources are described as challenging, while those that suggest high demands and low resources are perceived as threatening (Seginer, 2008). In general, challenge or threat can take place in situations such as delivering a speech, taking a test, sports competitions or performing with another person on a cooperative or competitive task.The challenge appraisal suggests that with effort, the demands of the situation can be overcome (Lazarus et al. , 1980; Park and Folkman, 1997). On the other hand, threat appraisal indicates potential danger to one's well-being or self-esteem (Lazarus, 1991; Lazarus and Folkman, 1984), as well as low confidence in one's ability to cope with the threat (Bandura, 1997; Lazarus, 1991; Lazarus and Folkman, 1984). Different studies (Blascovich et al. , 2002; Blascovich and Mendes, 2000; Lazarus and Folkman, 1984; Lazarus et al. , 1980) have found that challenge leads to positive feelings associated with enjoyment, better performance, eagerness and anticipation of personal rewards or benefits. Several studies which focussed on the threat and challenge variable were carried out in the library and information science environment as well (Aharony, 2009, 2011).Self-efficacyAn additional variable which may influence individuals' behavioural intention to use cloud computing is self-efficacy. The concept of self-efficacy was developed in the discipline of "social learning theory" by Bandura (1997). Self-efficacy addresses individuals' beliefs that they possess the resources and skills needed to perform and succeed in a specific task. Therefore, individuals' previous performance and their perceptions of relevant resources available may influence self-efficacy beliefs (Bandura, 1997). Self-efficacy is not just an ability perception, it encompasses the motivation and effort required to complete the task and it helps determine which activities are required, the effort in pursuing these activities and persistence when facing obstacles (Bandura, 1986, 1997). The construct of self-efficacy is made up of four principal sources of information:"mastery experience" refers to previous experience, including success and failure; "vicarious experience" addresses observing the performances, successes and failures of others;"social persuasion" includes verbal persuasion from peers, colleagues and relatives; and"physiological and emotional states" from which people judge their strengths, capabilities and vulnerabilities (Bandura, 1986, 1994, 1995).As self-efficacy is based on self-perceptions regarding different behaviours, it is considered to be situation specific. In other words, a person may exhibit high levels of self-efficacy within one domain, while exhibiting low levels within another (Cassidy and Eachus, 2002). Thus, self-efficacy has generated research in various disciplines such as medicine, business, psychology and education (Kear, 2000; Lev, 1997; Schunk, 1985; Koul and Rubba, 1999). Computer self-efficacy is a sub-field of self-efficacy. It is defined as one's perceived ability to accomplish a task with the use of a computer (Compeau and Higgins, 1995). Various studies have noted that training and experience play important roles in computer self-efficacy (Compeau and Higgins, 1995; Kinzie et al. , 1994; Stone and Henry, 2003). Several studies have investigated the effect of computer self-efficacy on computer training performance (Compeau and Higgins, 1995) and on IT use (Easley et al. , 2003).HypothesesBased on the study objectives and assuming that PEOU, personal innovativeness,cognitive appraisal and openness to experience may predict information professionals' behavioural intention to use cloud computing, the underlying assumptions of this study are as follows:H1. High scores in respondent PEOU will be associated with high scores in their behavioural intention to use cloud computing.H2. High scores in respondents' personal innovativeness will be associated with high scores in their behavioural intention to use cloud computing.H3. Low scores in respondents' threat and high scores in respondents' challenge will be associated with high scores in their behavioural intention to use cloud computing. H4. High scores in respondents' self-efficacy will be associated with high scores in their behavioural intention to use cloud computing.H5. High scores in respondents' openness to experience will be associated with high scores in their behavioural intention to use cloud computing.H6. High scores in respondents' computer competence and in social media use will be associated with high scores in their behavioural intention to use cloud computing. MethodologyData collectionThe research was conducted in Israel during the second semester of the 2013 academic year and encompassed two groups of information professionals: librarians and information specialists. The researchers sent a message and a questionnaire to an Israeli library and information science discussion group named "safranym", which included school, public and academic librarians, and to an Israeli information specialist group named "I-fish", which consists of information specialists that work in different organizations. Researchers explained the study's purpose and asked their members to complete the questionnaire. These two discussion groups consist of about 700 members; 140 responses were received, giving a reply percentage of 20 per cent. Data analysisOf the participants, 25 (17.9 per cent) were male and 115 (82.1 per cent) were female. Their average age was 46.3 years.MeasuresThe current study is based on quantitative research. Researchers used seven questionnaires to gather the following data: personal details, computer competence, attitudes towards cloud computing, behavioural intention, openness to experience, cognitive appraisal and self-efficacy.The personal details questionnaire had two statements. The computer competence questionnaire consisted of two statements rated on a 5-point Likert scale (1 = strongest disagreement; 5 = strongest agreement). The cloud computing attitude questionnaire, based on Liuet al. (2010), was modified for this study and consisted of six statements rated on a seven-point Likert scale (1 = strongest disagreement; 7 = strongest agreement). A principal components factor analysis using Varimax rotation with Kaiser Normalization was conducted and explained 82.98 per cent of the variance. Principal components factor analysis revealed two distinct factors. The first related to information professionals' personal innovativeness (items 2, 3 and 5), and the second to information professionals' perceptions about cloud computing ease ofuse (PEOU) (items 1, 4, and 6); the values of Cronbach's Alpha were 0.89 and 0.88, respectively.The behavioural intention questionnaire, based on Liu et al. (2010), was modified for this study and consisted of three statements rated on a six-point Likert scale (1 = strongest disagreement; 6 = strongest agreement). Its Cronbach's Alpha was 0.79. The openness to experience questionnaire was derived from the Big Five questionnaire (John et al. , 1991) and consisted of eight statements rated on a five-point Likert scale (1 = strongest disagreement; 5 = strongest agreement); Cronbach's Alpha was 0.81. The cognitive appraisal questionnaire measured information professionals' feelings of threat versus challenge when confronted with new situations. It consisted of 10 statements rated on a six-point scale (1 = fully disagree; 6 = fully agree). This questionnaire was previously used (Aharony, 2009, 2011; Yekutiel, 1990) and consisted of two factors: threat (items 1, 2, 3, 5, 7 and 8) and challenge (items 4, 6, 9 and 10). Cronbach's Alpha was 0.70 for the threat factor and 0.89 for the challenge factor.The self-efficacy questionnaire was based on Askar and Umay's (2001) questionnaire and consisted of 18 statements rated on a five-point scale (1 = fully disagree; 5 = fully agree); Cronbach's Alpha was 0.96.FindingsTo examine the relationship between openness to experience, cognitive appraisal (threat, challenge and self-efficacy), TAM variables (personal innovativeness and PEOU), and behavioural intention to use cloud computing, researchers performed Pearson correlations, which are given in Table I.Table I presents significant correlations between research variables and the dependent variable (behavioural intention to use cloud computing). All correlations are positive, except the one between threat and behavioural intention to use cloud computing. Hence, the higher these measures, the greater the behavioural intention to use cloud computing. A significant negative correlation was found between threat and the dependent variable. Therefore, the more threatened respondents are, the lower is their behavioural intention to use cloud computing.Regarding the correlations between research variables, significant positive correlations were found between openness to experience and challenge, self-efficacy, personal innovativeness and PEOU. A significant negative correlation was found between openness to experience and threat. That is, the more open to experience respondents are, the more challenged they are, the higher is their self-efficacy, personal innovativeness, and PEOU and the less threatened they are. In addition, significant negative correlations were found between threat and self-efficacy, personal innovativeness and PEOU. We can conclude that the more threatened respondents are, the less they are self-efficient, personally innovative and the less they perceive cloud computing as easy to use. Significant positive correlations were also found between self-efficacy and personal innovativeness and PEOU. Thus, the more self-efficient respondents are, the more personally innovative they are and the more they perceive cloud computing as easy to use.The study also examined two variables associated with computer competence:computer use and social media use. Table II presents correlations between these two variables and the other research variables.Significant, high correlations were found between computer competence variables and openness to experience, self-efficacy, personal innovativeness, PEOU and behavioural intention to use cloud computing. Hence, the higher respondents' computer competence, the more they are open to experience, self-efficient and personally innovative, and perceive cloud computing as easy to use, the higher is their behavioural intention to use cloud computing.Researchers also examined relationships with demographic variables. To examine the relationship between age and other research variables, the researchers performed Pearson correlations. A significant negative correlation was found between age and PEOU, r = -0.21, p < 0.05. We may assume that the younger the respondents are, the more they perceive cloud computing as easy to use. To examine whether there are differences between males and females concerning the research variables, a MANOV A was performed and did not reveal a significant difference between the two groups concerning research variables, F (7,130) = 1.88, p > 0.05.The researchers also conducted a hierarchical regression using behavioural intention to use cloud computing as a dependent variable. The predictors were entered as five steps:respondents' openness to experience;respondents' computer competence (computer use and social media use);cognitive appraisal (threat, challenge and self-efficacy);TAM variables (personal innovativeness and PEOU); andinteractions with the TAM variables.The entrance of the four first steps was forced, while the interactions were done according to their contribution to the explained variance of behavioural intention to use cloud computing. The regression explained 54 per cent of behavioural intention to use cloud computing. Table III presents the standardized and unstandardized coefficients of the hierarchical regression of respondents' behavioural intention to use cloud computing.The first step introduced the openness variable that contributed significantly by adding 13 per cent to the explained variance of behavioural intention to use cloud computing. The beta coefficient of the openness variable is positive; hence, the more open to experience respondents are, the higher is their behavioural intention to use cloud computing. The second step introduced the two computer competence variables (computer use and social media use) which contributed 5 per cent to the explained variance of behavioural intention. Of these two variables, only the social media variable contributed significantly and its beta coefficient was positive. In other words, the more respondents use social media, the higher is their behavioural intention to use cloud computing. Note that Pearson correlations found significant positive correlations between these two variables and behavioural intention to use cloud computing. It seems that because of the correlation between these two variables, r = 0.33, p < 0.001, the computer use variable did not contribute to the regression.As the third step, researchers added respondents' personal appraisal variables (threat and challenge, and self-efficacy), and this also contributed significantly by adding 25 per cent to the explained variance of behavioural intention. The beta coefficients of challenge and of self-efficacy were positive, while that of threat was negative. Therefore, we may conclude that the more respondents perceived themselves as challenged and self-efficient, and the less they perceived themselves as threatened, the higher is their behavioural intention to use cloud computing. The inclusion of this step caused a decrease in the [beta] size of the openness to experience variable that changed it into an insignificant one, and may suggest a possibility of mediation. Sobel tests indicated that self-efficacy mediates between openness to experience and behavioural intention (z = 4.68, p < 0.001). Hence, the more respondents are open to experience, the higher is their self-efficacy and, as a result, the higher is their behavioural intention to use cloud computing.The fourth step added the TAM variables (respondents' PEOU and personal innovation), and this also contributed significantly by adding 9 per cent to the explained variance of behavioural intention to use cloud computing. The beta coefficient of this variable was positive; therefore, the more respondents perceived themselves to be personally innovative and cloud computing as easy to use, the higher is their behavioural intention to use cloud computing. Note that in this step there was a decrease in the [beta] size of self-efficacy. Sobel tests indicated that of the two variables, PEOU mediates between self-efficacy and behavioural intention (z = 4.77, p < 0.001). Thus, the more respondents perceive themselves as self-efficient, the higher they perceive cloud computing's PEOU and, as a result, the higher is their behavioural intention to use it.As the fifth step, researchers added the interaction between computer use X personal innovativeness. This interaction added 2 per cent to the explained variance of behavioural intention to use cloud computing and is presented in Figure 1.Figure 1 shows a correlation between personal innovation and behavioural intention to use cloud computing among respondents who are low and high in computer use. This correlation is higher among respondents who are low in computer use, [beta] = . 40, p < 0.05, than among those who are high in computer use, [beta] = 0.04, p < 0.05. It seems that especially among participants who are low in computer use, the higher their personal innovativeness, the higher is their behavioural intention to use cloud computing.DiscussionThe present research explored the extent to which the TAM and personal characteristics, such as threat and challenge, self-efficacy and openness to experience, explain information professionals' perspectives on cloud computing. Researchers divided the study hypotheses into three categories. The first (consisting of H1 -H2 ) refers to the TAM, the second (H3 -H5 ) to personality characteristics and, finally, H6 to computer competence. All hypotheses were accepted. Regarding the first category of hypotheses, results show that both were accepted. Findings suggest that high scores in PEOU and personal innovativeness are associated with high scores in respondents' intention to adopt cloud computing. These findings can be associated with previous。

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云计算外文翻译参考文献(文档含中英文对照即英文原文和中文翻译)原文:Technical Issues of Forensic Investigations in Cloud Computing EnvironmentsDominik BirkRuhr-University BochumHorst Goertz Institute for IT SecurityBochum, GermanyRuhr-University BochumHorst Goertz Institute for IT SecurityBochum, GermanyAbstract—Cloud Computing is arguably one of the most discussedinformation technologies today. It presents many promising technological and economical opportunities. However, many customers remain reluctant to move their business IT infrastructure completely to the cloud. One of their main concerns is Cloud Security and the threat of the unknown. Cloud Service Providers(CSP) encourage this perception by not letting their customers see what is behind their virtual curtain. A seldomly discussed, but in this regard highly relevant open issue is the ability to perform digital investigations. This continues to fuel insecurity on the sides of both providers and customers. Cloud Forensics constitutes a new and disruptive challenge for investigators. Due to the decentralized nature of data processing in the cloud, traditional approaches to evidence collection and recovery are no longer practical. This paper focuses on the technical aspects of digital forensics in distributed cloud environments. We contribute by assessing whether it is possible for the customer of cloud computing services to perform a traditional digital investigation from a technical point of view. Furthermore we discuss possible solutions and possible new methodologies helping customers to perform such investigations.I. INTRODUCTIONAlthough the cloud might appear attractive to small as well as to large companies, it does not come along without its own unique problems. Outsourcing sensitive corporate data into the cloud raises concerns regarding the privacy and security of data. Security policies, companies main pillar concerning security, cannot be easily deployed into distributed, virtualized cloud environments. This situation is further complicated by the unknown physical location of the companie’s assets. Normally,if a security incident occurs, the corporate security team wants to be able to perform their own investigation without dependency on third parties. In the cloud, this is not possible anymore: The CSP obtains all the power over the environmentand thus controls the sources of evidence. In the best case, a trusted third party acts as a trustee and guarantees for the trustworthiness of the CSP. Furthermore, the implementation of the technical architecture and circumstances within cloud computing environments bias the way an investigation may be processed. In detail, evidence data has to be interpreted by an investigator in a We would like to thank the reviewers for the helpful comments and Dennis Heinson (Center for Advanced Security Research Darmstadt - CASED) for the profound discussions regarding the legal aspects of cloud forensics. proper manner which is hardly be possible due to the lackof circumstantial information. For auditors, this situation does not change: Questions who accessed specific data and information cannot be answered by the customers, if no corresponding logs are available. With the increasing demand for using the power of the cloud for processing also sensible information and data, enterprises face the issue of Data and Process Provenance in the cloud [10]. Digital provenance, meaning meta-data that describes the ancestry or history of a digital object, is a crucial feature for forensic investigations. In combination with a suitable authentication scheme, it provides information about who created and who modified what kind of data in the cloud. These are crucial aspects for digital investigations in distributed environments such as the cloud. Unfortunately, the aspects of forensic investigations in distributed environment have so far been mostly neglected by the research community. Current discussion centers mostly around security, privacy and data protection issues [35], [9], [12]. The impact of forensic investigations on cloud environments was little noticed albeit mentioned by the authors of [1] in 2009: ”[...] to our knowledge, no research has been published on how cloud computing environments affect digital artifacts,and on acquisition logistics and legal issues related to cloud computing env ironments.” This statement is also confirmed by other authors [34], [36], [40] stressing that further research on incident handling, evidence tracking and accountability in cloud environments has to be done. At the same time, massive investments are being made in cloud technology. Combined with the fact that information technology increasingly transcendents peoples’ private and professional life, thus mirroring more and more of peoples’actions, it becomes apparent that evidence gathered from cloud environments will be of high significance to litigation or criminal proceedings in the future. Within this work, we focus the notion of cloud forensics by addressing the technical issues of forensics in all three major cloud service models and consider cross-disciplinary aspects. Moreover, we address the usability of various sources of evidence for investigative purposes and propose potential solutions to the issues from a practical standpoint. This work should be considered as a surveying discussion of an almost unexplored research area. The paper is organized as follows: We discuss the related work and the fundamental technical background information of digital forensics, cloud computing and the fault model in section II and III. In section IV, we focus on the technical issues of cloud forensics and discuss the potential sources and nature of digital evidence as well as investigations in XaaS environments including thecross-disciplinary aspects. We conclude in section V.II. RELATED WORKVarious works have been published in the field of cloud security and privacy [9], [35], [30] focussing on aspects for protecting data in multi-tenant, virtualized environments. Desired security characteristics for current cloud infrastructures mainly revolve around isolation of multi-tenant platforms [12], security of hypervisors in order to protect virtualized guest systems and secure network infrastructures [32]. Albeit digital provenance, describing the ancestry of digital objects, still remains a challenging issue for cloud environments, several works have already been published in this field [8], [10] contributing to the issues of cloud forensis. Within this context, cryptographic proofs for verifying data integrity mainly in cloud storage offers have been proposed,yet lacking of practical implementations [24], [37], [23]. Traditional computer forensics has already well researched methods for various fields of application [4], [5], [6], [11], [13]. Also the aspects of forensics in virtual systems have been addressed by several works [2], [3], [20] including the notionof virtual introspection [25]. In addition, the NIST already addressed Web Service Forensics [22] which has a huge impact on investigation processes in cloud computing environments. In contrast, the aspects of forensic investigations in cloud environments have mostly been neglected by both the industry and the research community. One of the first papers focusing on this topic was published by Wolthusen [40] after Bebee et al already introduced problems within cloud environments [1]. Wolthusen stressed that there is an inherent strong need for interdisciplinary work linking the requirements and concepts of evidence arising from the legal field to what can be feasibly reconstructed and inferred algorithmically or in an exploratory manner. In 2010, Grobauer et al [36] published a paper discussing the issues of incident response in cloud environments - unfortunately no specific issues and solutions of cloud forensics have been proposed which will be done within this work.III. TECHNICAL BACKGROUNDA. Traditional Digital ForensicsThe notion of Digital Forensics is widely known as the practice of identifying, extracting and considering evidence from digital media. Unfortunately, digital evidence is both fragile and volatile and therefore requires the attention of special personnel and methods in order to ensure that evidence data can be proper isolated and evaluated. Normally, the process of a digital investigation can be separated into three different steps each having its own specificpurpose:1) In the Securing Phase, the major intention is the preservation of evidence for analysis. The data has to be collected in a manner that maximizes its integrity. This is normally done by a bitwise copy of the original media. As can be imagined, this represents a huge problem in the field of cloud computing where you never know exactly where your data is and additionallydo not have access to any physical hardware. However, the snapshot technology, discussed in section IV-B3, provides a powerful tool to freeze system states and thus makes digital investigations, at least in IaaS scenarios, theoretically possible.2) We refer to the Analyzing Phase as the stage in which the data is sifted and combined. It is in this phase that the data from multiple systems or sources is pulled together to create as complete a picture and event reconstruction as possible. Especially in distributed system infrastructures, this means that bits and pieces of data are pulled together for deciphering the real story of what happened and for providing a deeper look into the data.3) Finally, at the end of the examination and analysis of the data, the results of the previous phases will be reprocessed in the Presentation Phase. The report, created in this phase, is a compilation of all the documentation and evidence from the analysis stage. The main intention of such a report is that it contains all results, it is complete and clear to understand. Apparently, the success of these three steps strongly depends on the first stage. If it is not possible to secure the complete set of evidence data, no exhaustive analysis will be possible. However, in real world scenarios often only a subset of the evidence data can be secured by the investigator. In addition, an important definition in the general context of forensics is the notion of a Chain of Custody. This chain clarifies how and where evidence is stored and who takes possession of it. Especially for cases which are brought to court it is crucial that the chain of custody is preserved.B. Cloud ComputingAccording to the NIST [16], cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal CSP interaction. The new raw definition of cloud computing brought several new characteristics such as multi-tenancy, elasticity, pay-as-you-go and reliability. Within this work, the following three models are used: In the Infrastructure asa Service (IaaS) model, the customer is using the virtual machine provided by the CSP for installing his own system on it. The system can be used like any other physical computer with a few limitations. However, the additive customer power over the system comes along with additional security obligations. Platform as a Service (PaaS) offerings provide the capability to deploy application packages created using the virtual development environment supported by the CSP. For the efficiency of software development process this service model can be propellent. In the Software as a Service (SaaS) model, the customer makes use of a service run by the CSP on a cloud infrastructure. In most of the cases this service can be accessed through an API for a thin client interface such as a web browser. Closed-source public SaaS offers such as Amazon S3 and GoogleMail can only be used in the public deployment model leading to further issues concerning security, privacy and the gathering of suitable evidences. Furthermore, two main deployment models, private and public cloud have to be distinguished. Common public clouds are made available to the general public. The corresponding infrastructure is owned by one organization acting as a CSP and offering services to its customers. In contrast, the private cloud is exclusively operated for an organization but may not provide the scalability and agility of public offers. The additional notions of community and hybrid cloud are not exclusively covered within this work. However, independently from the specific model used, the movement of applications and data to the cloud comes along with limited control for the customer about the application itself, the data pushed into the applications and also about the underlying technical infrastructure.C. Fault ModelBe it an account for a SaaS application, a development environment (PaaS) or a virtual image of an IaaS environment, systems in the cloud can be affected by inconsistencies. Hence, for both customer and CSP it is crucial to have the ability to assign faults to the causing party, even in the presence of Byzantine behavior [33]. Generally, inconsistencies can be caused by the following two reasons:1) Maliciously Intended FaultsInternal or external adversaries with specific malicious intentions can cause faults on cloud instances or applications. Economic rivals as well as former employees can be the reason for these faults and state a constant threat to customers and CSP. In this model, also a malicious CSP is included albeit he isassumed to be rare in real world scenarios. Additionally, from the technical point of view, the movement of computing power to a virtualized, multi-tenant environment can pose further threads and risks to the systems. One reason for this is that if a single system or service in the cloud is compromised, all other guest systems and even the host system are at risk. Hence, besides the need for further security measures, precautions for potential forensic investigations have to be taken into consideration.2) Unintentional FaultsInconsistencies in technical systems or processes in the cloud do not have implicitly to be caused by malicious intent. Internal communication errors or human failures can lead to issues in the services offered to the costumer(i.e. loss or modification of data). Although these failures are not caused intentionally, both the CSP and the customer have a strong intention to discover the reasons and deploy corresponding fixes.IV. TECHNICAL ISSUESDigital investigations are about control of forensic evidence data. From the technical standpoint, this data can be available in three different states: at rest, in motion or in execution. Data at rest is represented by allocated disk space. Whether the data is stored in a database or in a specific file format, it allocates disk space. Furthermore, if a file is deleted, the disk space is de-allocated for the operating system but the data is still accessible since the disk space has not been re-allocated and overwritten. This fact is often exploited by investigators which explore these de-allocated disk space on harddisks. In case the data is in motion, data is transferred from one entity to another e.g. a typical file transfer over a network can be seen as a data in motion scenario. Several encapsulated protocols contain the data each leaving specific traces on systems and network devices which can in return be used by investigators. Data can be loaded into memory and executed as a process. In this case, the data is neither at rest or in motion but in execution. On the executing system, process information, machine instruction and allocated/de-allocated data can be analyzed by creating a snapshot of the current system state. In the following sections, we point out the potential sources for evidential data in cloud environments and discuss the technical issues of digital investigations in XaaS environmentsas well as suggest several solutions to these problems.A. Sources and Nature of EvidenceConcerning the technical aspects of forensic investigations, the amount of potential evidence available to the investigator strongly diverges between thedifferent cloud service and deployment models. The virtual machine (VM), hosting in most of the cases the server application, provides several pieces of information that could be used by investigators. On the network level, network components can provide information about possible communication channels between different parties involved. The browser on the client, acting often as the user agent for communicating with the cloud, also contains a lot of information that could be used as evidence in a forensic investigation. Independently from the used model, the following three components could act as sources for potential evidential data.1) Virtual Cloud Instance: The VM within the cloud, where i.e. data is stored or processes are handled, contains potential evidence [2], [3]. In most of the cases, it is the place where an incident happened and hence provides a good starting point for a forensic investigation. The VM instance can be accessed by both, the CSP and the customer who is running the instance. Furthermore, virtual introspection techniques [25] provide access to the runtime state of the VM via the hypervisor and snapshot technology supplies a powerful technique for the customer to freeze specific states of the VM. Therefore, virtual instances can be still running during analysis which leads to the case of live investigations [41] or can be turned off leading to static image analysis. In SaaS and PaaS scenarios, the ability to access the virtual instance for gathering evidential information is highly limited or simply not possible.2) Network Layer: Traditional network forensics is knownas the analysis of network traffic logs for tracing events that have occurred in the past. Since the different ISO/OSI network layers provide several information on protocols and communication between instances within as well as with instances outside the cloud [4], [5], [6], network forensics is theoretically also feasible in cloud environments. However in practice, ordinary CSP currently do not provide any log data from the network components used by the customer’s instances or applications. For instance, in case of a malware infection of an IaaS VM, it will be difficult for the investigator to get any form of routing information and network log datain general which is crucial for further investigative steps. This situation gets even more complicated in case of PaaS or SaaS. So again, the situation of gathering forensic evidence is strongly affected by the support the investigator receives from the customer and the CSP.3) Client System: On the system layer of the client, it completely depends on the used model (IaaS, PaaS, SaaS) if and where potential evidence could beextracted. In most of the scenarios, the user agent (e.g. the web browser) on the client system is the only application that communicates with the service in the cloud. This especially holds for SaaS applications which are used and controlled by the web browser. But also in IaaS scenarios, the administration interface is often controlled via the browser. Hence, in an exhaustive forensic investigation, the evidence data gathered from the browser environment [7] should not be omitted.a) Browser Forensics: Generally, the circumstances leading to an investigation have to be differentiated: In ordinary scenarios, the main goal of an investigation of the web browser is to determine if a user has been victim of a crime. In complex SaaS scenarios with high client-server interaction, this constitutes a difficult task. Additionally, customers strongly make use of third-party extensions [17] which can be abused for malicious purposes. Hence, the investigator might want to look for malicious extensions, searches performed, websites visited, files downloaded, information entered in forms or stored in local HTML5 stores, web-based email contents and persistent browser cookies for gathering potential evidence data. Within this context, it is inevitable to investigate the appearance of malicious JavaScript [18] leading to e.g. unintended AJAX requests and hence modified usage of administration interfaces. Generally, the web browser contains a lot of electronic evidence data that could be used to give an answer to both of the above questions - even if the private mode is switched on [19].B. Investigations in XaaS EnvironmentsTraditional digital forensic methodologies permit investigators to seize equipment and perform detailed analysis on the media and data recovered [11]. In a distributed infrastructure organization like the cloud computing environment, investigators are confronted with an entirely different situation. They have no longer the option of seizing physical data storage. Data and processes of the customer are dispensed over an undisclosed amount of virtual instances, applications and network elements. Hence, it is in question whether preliminary findings of the computer forensic community in the field of digital forensics apparently have to be revised and adapted to the new environment. Within this section, specific issues of investigations in SaaS, PaaS and IaaS environments will be discussed. In addition, cross-disciplinary issues which affect several environments uniformly, will be taken into consideration. We also suggest potential solutions to the mentioned problems.1) SaaS Environments: Especially in the SaaS model, the customer does notobtain any control of the underlying operating infrastructure such as network, servers, operating systems or the application that is used. This means that no deeper view into the system and its underlying infrastructure is provided to the customer. Only limited userspecific application configuration settings can be controlled contributing to the evidences which can be extracted fromthe client (see section IV-A3). In a lot of cases this urges the investigator to rely on high-level logs which are eventually provided by the CSP. Given the case that the CSP does not run any logging application, the customer has no opportunity to create any useful evidence through the installation of any toolkit or logging tool. These circumstances do not allow a valid forensic investigation and lead to the assumption that customers of SaaS offers do not have any chance to analyze potential incidences.a) Data Provenance: The notion of Digital Provenance is known as meta-data that describes the ancestry or history of digital objects. Secure provenance that records ownership and process history of data objects is vital to the success of data forensics in cloud environments, yet it is still a challenging issue today [8]. Albeit data provenance is of high significance also for IaaS and PaaS, it states a huge problem specifically for SaaS-based applications: Current global acting public SaaS CSP offer Single Sign-On (SSO) access control to the set of their services. Unfortunately in case of an account compromise, most of the CSP do not offer any possibility for the customer to figure out which data and information has been accessed by the adversary. For the victim, this situation can have tremendous impact: If sensitive data has been compromised, it is unclear which data has been leaked and which has not been accessed by the adversary. Additionally, data could be modified or deleted by an external adversary or even by the CSP e.g. due to storage reasons. The customer has no ability to proof otherwise. Secure provenance mechanisms for distributed environments can improve this situation but have not been practically implemented by CSP [10]. Suggested Solution: In private SaaS scenarios this situation is improved by the fact that the customer and the CSP are probably under the same authority. Hence, logging and provenance mechanisms could be implemented which contribute to potential investigations. Additionally, the exact location of the servers and the data is known at any time. Public SaaS CSP should offer additional interfaces for the purpose of compliance, forensics, operations and security matters to their customers. Through an API, the customers should have the ability to receive specific information suchas access, error and event logs that could improve their situation in case of aninvestigation. Furthermore, due to the limited ability of receiving forensic information from the server and proofing integrity of stored data in SaaS scenarios, the client has to contribute to this process. This could be achieved by implementing Proofs of Retrievability (POR) in which a verifier (client) is enabled to determine that a prover (server) possesses a file or data object and it can be retrieved unmodified [24]. Provable Data Possession (PDP) techniques [37] could be used to verify that an untrusted server possesses the original data without the need for the client to retrieve it. Although these cryptographic proofs have not been implemented by any CSP, the authors of [23] introduced a new data integrity verification mechanism for SaaS scenarios which could also be used for forensic purposes.2) PaaS Environments: One of the main advantages of the PaaS model is that the developed software application is under the control of the customer and except for some CSP, the source code of the application does not have to leave the local development environment. Given these circumstances, the customer obtains theoretically the power to dictate how the application interacts with other dependencies such as databases, storage entities etc. CSP normally claim this transfer is encrypted but this statement can hardly be verified by the customer. Since the customer has the ability to interact with the platform over a prepared API, system states and specific application logs can be extracted. However potential adversaries, which can compromise the application during runtime, should not be able to alter these log files afterwards. Suggested Solution:Depending on the runtime environment, logging mechanisms could be implemented which automatically sign and encrypt the log information before its transfer to a central logging server under the control of the customer. Additional signing and encrypting could prevent potential eavesdroppers from being able to view and alter log data information on the way to the logging server. Runtime compromise of an PaaS application by adversaries could be monitored by push-only mechanisms for log data presupposing that the needed information to detect such an attack are logged. Increasingly, CSP offering PaaS solutions give developers the ability to collect and store a variety of diagnostics data in a highly configurable way with the help of runtime feature sets [38].3) IaaS Environments: As expected, even virtual instances in the cloud get compromised by adversaries. Hence, the ability to determine how defenses in the virtual environment failed and to what extent the affected systems havebeen compromised is crucial not only for recovering from an incident. Also forensic investigations gain leverage from such information and contribute to resilience against future attacks on the systems. From the forensic point of view, IaaS instances do provide much more evidence data usable for potential forensics than PaaS and SaaS models do. This fact is caused throughthe ability of the customer to install and set up the image for forensic purposes before an incident occurs. Hence, as proposed for PaaS environments, log data and other forensic evidence information could be signed and encrypted before itis transferred to third-party hosts mitigating the chance that a maliciously motivated shutdown process destroys the volatile data. Although, IaaS environments provide plenty of potential evidence, it has to be emphasized that the customer VM is in the end still under the control of the CSP. He controls the hypervisor which is e.g. responsible for enforcing hardware boundaries and routing hardware requests among different VM. Hence, besides the security responsibilities of the hypervisor, he exerts tremendous control over how customer’s VM communicate with the hardware and theoretically can intervene executed processes on the hosted virtual instance through virtual introspection [25]. This could also affect encryption or signing processes executed on the VM and therefore leading to the leakage of the secret key. Although this risk can be disregarded in most of the cases, the impact on the security of high security environments is tremendous.a) Snapshot Analysis: Traditional forensics expect target machines to be powered down to collect an image (dead virtual instance). This situation completely changed with the advent of the snapshot technology which is supported by all popular hypervisors such as Xen, VMware ESX and Hyper-V.A snapshot, also referred to as the forensic image of a VM, providesa powerful tool with which a virtual instance can be clonedby one click including also the running system’s mem ory. Due to the invention of the snapshot technology, systems hosting crucial business processes do not have to be powered down for forensic investigation purposes. The investigator simply creates and loads a snapshot of the target VM for analysis(live virtual instance). This behavior is especially important for scenarios in which a downtime of a system is not feasible or practical due to existing SLA. However the information whether the machine is running or has been properly powered down is crucial [3] for the investigation. Live investigations of running virtual instances become more common providing evidence data that。

云计算外文翻译参考文献

云计算英文论文

云计算英文论文Cloud computing1. IntroductionCloud computing is known as the most anticipated technological revolution over the entire world, and the reason why cloud computing has brought widespread attention is that it not only represent a new technology appeared, but also lead to the entire industry to change. Therefore, the ranking of national competitiveness will change accordingly.2. Cloud computing definition and applicationThe concept of cloud computing is put forward by Google, which is a beautiful network applications mode. Based on increasing in Internet-related services and delivery models, it provides dynamic, easy to expand and virtualized resources usually through the Internet. This service can be related to IT, software and Internet; other services can be supported too. It means that computing power can be circulation as a commodity. As we expect, more and more enterprise use this new technology into the practical application, such as webmail, Gmail and apple store use it for computing and storage. With the technology development, cloud computing has given full play to it roles, Industry, Newcomers and Media buy computer technology to make network cheaper, faster, convenient and better control.Cloud computing is used in the distributed computers by computing, rather than the local computer or remote server, the operation of the enterprise data center and the Internet are more similar. This process allows companies to be able to switch resources to the urgent application according to computer and storage systems demand access. This means that the computingpower can be negotiable, like water and electricity, access convenient andlow-cost. The biggest difference is that it is transmitted via the Internet.3. AdvantageBy providing a large and secure data storage centers for cloud computing, users do not have to worry about their own data which be lost for some reason or destroyed by computer virus invasion, such as some data stored in the hard disk data will be lost by the computer damaged and virus invasion, so that users cannot access the data and recover data, another disadvantage may also appear when other people use your computer steal the user's computer, such as personal confidential information and business data loss. “Gate of blue photos " is a typical insecure example. If user uploads the photos through the Interne into data storage center, it may have less chance to access personal confidential information. And with cloud computing development, for the user, these services are free of charge, for the future, advanced technology will certainly become a cash cow for merchants, and for the country, the computing power is also able to reflect a country technological level and the level of overall national strength.As the user data stored in the cloud storage center, can reduce customer demand for thehardware level, coupled with cloud computing extremely powerful computing capability, it can enable users to call data more convenient and quick if can add the high-speed networks. Users no longer need replace the computer caused by having no enough hard disk space and CPU computing power. Otherwise, users only surf the Internet to access cloud storage center andthen easily access the data.As powerful data storage centers, the most wonderful features is sharing data. No matter computer data or a variety of communication devices such as mobile phones and PDA. When some damaged appeared in your phone, lost or replaced in order to chase the trend of the times and phone, data copy is a tedious thing. However, in another way, you can solve the entire problem through cloud storage center.Cloud computing services will create more millions of jobs. On March 6, a news show that show that Microsoft statement a commissioned research conducted by theworld-renowned market analyst firm IDC. The research show that cloud computing will create nearly 14 million new jobs worldwide in 2015. And also predicted that it can stimulate IT innovation and brought the new revenue which can reach about $ 1.1 trillion, coupled with cloud computing efficiency substantially promote, the organization will increasere-investment and work opportunity. IDC chief research officer , as senior vice president, John F. Gantz said: "For most organizations, there is no doubt that cloud computing will significantly enhance the return on investment and flexibility, reduce investment costs, and bring revenue growth multiplied as well, we usually believe that cloud computing will reduce jobs by mistake, the opposite, it can create a lot of jobs around the world, no matter emerging market, small cities and small businesses will increase employment opportunities and get benefit from cloud computing. "14. DisadvantageWith large storage center, cloud computing strongly urges powerful hardware conditions and safety measures, whichincluding a large storage space and powerful computing capabilities. As today's scientific and technological rapid develop, user increased, hardware capabilities become one of the necessary condition for development, moreover, the cloud should be kept to enhance their computing power and storage space, and endless. Security measures also include two aspects; firstly, it is necessary to ensure that user data not damaged, lost, and stolen by others, which requires a strong IT team to full range of maintenance and strict access management strategies. Some information has been confirmed, the existing cloud computing service provider still cannot guarantee no similar problem occurs. For users, this is a very serious problem. Another security issue is a natural or unnatural disaster, also can cause the storage center damaged, can cause users cannot access.Cloud computing services are available via the Internet, therefore, user usually have high demands for network access speed, although domestic access speeds improve fast, but compared with LAN, it will appear delays, incomparable, In addition, no network, the user will not be able to access cloud services.The rapid rise of Cloud technology, in another perspective, also restricts the speed of development, which also lead to lower demand for high-end devices. It is the basic reason1Reference[1]why can restrict terminal development. Customer needs, determine the business requirements for products, if customers reduce the terminal's hardware and software requirements, the business will be greatly reduced the degree of product development and restricted the pace of the terminal technology development.5. Cloud computing devel opment in chinaIn China, Cloud computing services have been in a state of rapid development. Looking at the cloud computing industry, government accounted for a major position, and in the market they have also been given the affirmation of the cloud computing. The cloud computing services major support for government agencies and enterprises, therefore, the personal cloud computing services in China is a potential big cake and wait for people enjoying. However because of the domestic personal network speed upgrade too slow, and relevant government network supervision, the development of personal cloud computing services will have more difficult. Due to excessive speculation in the international aspects, in our country, a lot of companies want to get some benefit from the rapid development of technology, exploit cloud computing services projects and promote enterprise development. My personal view is that enterprises should do some preparation, judge it’s good or bad and prospects, as well as estimate the strength of the company and do their best. Through access to information discovery, cloud computing as a technology trend has been got the world's attention, but look at the forefront of foreign enterprise closely, we can easily find almost no companies start the business rely on cloud computing; cloud computing applications and related research are also mostly implement by those large enterprise with more funds. As the prospects for development, for the enterprise, profit is the most critical issues.Although cloud computing has been integrated into our lives, but the domestic development of cloud computing is still confronted with many problems, such as lack of user awareness, migration risk, lack of standards, user locks, security and dataprivacy, safety, standards, data privacy has become a topic of which we are most concerned.Nowadays, people still lack of comprehensive, systematic cognitive of Cloud computing, part of them just imitate completely others and foreign experience, ignoring the specific conditions in our country, resulting in spending much money, but failed to alleviate the complex IT issues. In the original data center, hardware is relatively independent, but migrates to cloud computing data center, systematic assessment and scientific analysis must be essential.Otherwise it may lead to the hardware platform not achieve the proper effect and even the collapse of the application system. Cloud computing products is quite diversity, but the birth of the cloud standard is extremely difficult, the major manufacturers just have their own standards, the Government is also involved, both of them are striving to be able to dominate the major position, so more efforts are still needed. The other faced challenge is how to provide users legitimate services is also very important, except the risks in the systems. Compared with traditional data centers, it cloud provided more services diversity, which also bring more difficult to control. Therefore, analyze the needs of users, reasonable, enforceable service level agreements (SLAs) provided will help users establish confidence in cloud computing services in a large extent. Security problems can be said that a key factor in cloud computing landing. Amazon failure made people deep in thought, which is just one factor of the security.The other security risks include user privacy protection, data sovereignty, disaster recovery, and even illegal interests caused by some hackers. In the era of cloud computing, there will be more user privacy data placed in the clouds, more data, and morevalue. Most importantly, lack of user’s trust will lead to cloud computing landing.Although the development road is very far away, but the Government has been giving support and encouragement. In 2012 Cloud Computing Industry Development Forum was successfully held, which is strong evidence. At the meeting, as a CCID Consulting, cloud industry observers, as well as excellence forerunner of the cloud computing industry, Microsoft, with their co-operation, complete the first "China Cloud economic development Report”, which shows that in-depth interpretation the content of cloud formation and specify the key factor of economic development in the cloud. The result obtained based on thelong-term observation and in-depth research on cloud computing industry. In addition, the participants share their own point about industry environment, market situation, technology development, application service elements and so on; they also can come up with any question about the cloud computing, expects will do their best to give analysis and authoritative view.26. Future prospectsAbout cloud computing services future prospects, in my opinion, it will have great potential, we can find out the answer from the action of the giants of the major IT companies, but now it still in a state of excessive speculation. Many small companies have no any knowledge and experience about it, and still want to exploit the services project. I don’t think it is a sagacious decision. in fact, for current state, I maintained a cautious attitude, as a corporate without strong financial strength, I think they should focus on business development and growth, in order to reduce unnecessary waste of resources, they can develop and useit when the technology get mature stage.Finally, have to say that the future of the cloud computing, it not only can provide unlimited possibilities for storage, read, and manage data using the network. But also provides us infinite computing power because of its huge storage space, there is no powerful computing capabilities, it cannot provide users with easy and fast. In one word, the development potential of cloud computing is endless.7. Reference[1] Microsoft news, Cloud computing services will create more millions of jobs.2012-03-06 2Reference[2]。

云计算外文文献+翻译

云计算外文文献+翻译1. 引言云计算是一种基于互联网的计算方式，它通过共享的计算资源提供各种服务。

随着云计算的普及和应用，许多研究者对该领域进行了深入的研究。

本文将介绍一篇外文文献，探讨云计算的相关内容，并提供相应的翻译。

2. 外文文献概述作者：Antonio Fernández Anta, Chryssis Georgiou, Evangelos Kranakis出版年份：2019年该外文文献主要综述了云计算的发展和应用。

文中介绍了云计算的基本概念，包括云计算的特点、架构、服务模型以及云计算的挑战和前景。

3. 研究内容该研究综述了云计算技术的基本概念和相关技术。

文中首先介绍了云计算的定义和其与传统计算的比较，深入探讨了云计算的优势和不足之处。

随后，文中介绍了云计算的架构，包括云服务提供商、云服务消费者和云服务的基本组件。

在架构介绍之后，文中提供了云计算的三种服务模型：基础设施即服务（IaaS）、平台即服务（PaaS）和软件即服务（SaaS）。

每种服务模型都从定义、特点和应用案例方面进行了介绍，并为读者提供了更深入的了解。

此外，文中还讨论了云计算的挑战，包括安全性、隐私保护、性能和可靠性等方面的问题。

同时，文中也探讨了云计算的前景和未来发展方向。

4. 文献翻译《云计算：一项调查》是一篇全面介绍云计算的文献。

它详细解释了云计算的定义、架构和服务模型，并探讨了其优势、不足和挑战。

此外，该文献还对云计算的未来发展进行了预测。

对于研究云计算和相关领域的读者来说，该文献提供了一个很好的参考资源。

它可以帮助读者了解云计算的基本概念、架构和服务模型，也可以引导读者思考云计算面临的挑战和应对方法。

5. 结论。

物联网外文文献翻译

物联网外文文献翻译
物联网是一个由许多设备彼此连接而形成的网络，这些设备可以是智能手机、传感器、汽车等。

物联网允许设备之间相互通信和交换数据，从而实现更智能、更高效和更安全的生活。

在物联网领域，一些外文文献对于我们的研究和研究非常有帮助。

以下是一些常见的物联网外文文献：
- "A Survey on Internet of Things From Industrial Market Perspective"：这篇论文介绍了物联网的概念、应用和市场现状，并分析了物联网在未来的趋势。

- "Big Data Analytics for IoT-Based Smart Environments: A Survey"：文章描述了如何使用大数据分析来处理物联网设备所产生的数据，并探讨了这种技术如何应用于智能环境中。

- "A Review of Smart Cities Based on the Internet of Things Concept"：这篇综述了物联网在智慧城市中的应用，并对物联网在智慧城市化中的挑战和机遇进行了讨论。

通过阅读这些文献，我们可以更深入地了解物联网的应用、市场和发展趋势，并且了解如何将物联网技术应用到实际生活中。

超越台式机一个关于云计算的介绍毕业论文外文翻译

超越台式机一个关于云计算的介绍毕业论文外文翻译翻译部分英文原文 Beyond the Desktop: An Introduction to Cloud Computing Michael Miller In a world that sees new technological trends bloom and fade on almost a dailybasis one new trend promises more longevity. This trend is called cloud computingand it will change the way you use your computer and the Internet. Cloud computing portends a major change in how we store information and runapplications. Instead of running program sand data on an individual desktop computereverything is hosted in the “cloud”—a nebulous assemblage of computers and serversaccessed via the Internet. Cloud computing lets you access all your applications anddocuments from anywhere in the world freeing you from the confines of the desktopand making it easier for group members in different locations to collaborate.PART 1 Understanding Cloud Computing The emergence of cloud computing is the computing equivalent of the electricityrevolution of a century ago. Before the advent of electrical utilities every farm andbusiness produced its own electricity from freestanding generators. After the electricalgrid was created farms and businesses shut down their generators and boughtelectricity from the utilities at a much lower price and with much greater reliabilitythan they could produce on their own. Look for the same type of revolution to occur as cloud computing takes hold.The desktop-centric notion of computing that we hold today is bound to fall by thewayside as we come to expect the universal access 24/7 reliability andubiquitouscollaboration promised by cloud computing. It is the way of the future.Cloud Computing: What It Is—and What It Isn’t With traditional desktopcomputing you run copies of software programs on eachcomputer you own. The documents you create are stored on the computer on whichthey were created. Although documents can be accessed from other computers on thenetwork they can’t be accessed by computers outside the network. The whole scene is PC-centric. With cloud computing the software programs you use aren’t run from yourpersonal computer but are rather stored on servers accessed via the Internet. If yourcomputer crashes the software is still available for others to use. Same goes for thedocuments you create they’re stored on a collection of servers accessed via theInternet. Anyone with permission can not only access the documents but can also editand collaborate on those documents in real time. Unlike traditional computing thiscloud computing model isn’t PC-centric it’sdocument-centric. Which PC you use to access a document simplyisn’t important. Butthat’s a simplification. Let’s look in more detail at what cloud computingis—and just asimportant what it isn’t.What Cloud Computing Isn’t First cloud computing isn’t network computing. With networkcomputing applications/documents are hosted on a single company’s server and accessed overthe company’s network. Cloud computing is a lot biggerthan that. It encompassesmultiple companies multiple servers andmultiple networks. Plus unlike networkcomputing cloud services and storage are accessible from anywhere in the world overan Internet connection with network computing access is over the company’snetwork only. Cloud computing also isn’t traditional outsourcing where a company farms outsubcontracts its computing services to an outside firm. While an outsourcing firmmight host a company’s data or applications those documents and programs are onlyaccessible to the company’s employees via the company’s network not to the entireworld via the Internet. So despite superficial similarities networking computing and outsourcing are notcloud computing.What Cloud Computing Is Key to the definition of cloud computing is the “cloud” itself. For our purposesthe cloud is a large group of interconnected computers. These computers can bepersonal computers ornetwork servers they can be public or private. For example Google hosts a cloud that consists of both smallish PCs and largerservers. Google’s cloud is a private one that is Google owns it that is publiclyaccessible by Google’s users. This cloud of computers extends beyond a single company or enterprise. Theapplications and data served by the cloud are available to broad group of userscross-enterprise and cross-platform. Access is via the Internet. Any authorized usercan access these docs and apps from any computer over any Internet connection. Andto the user the technology and infrastructure behind the cloud is invisible. It isn’t apparent and in most casesdoesn’t matter whether cloud services arebased on HTTP HTML XML JavaScript or other specific technologies. _ Cloud computing is user-centric. Once you as a user are connected to the cloudwhatever is stored there—documents messages images applicationswhatever—becomes yours. In addition not only is the data yours but you can alsoshare it with others. In effect any device that accesses your data in the cloud alsobecomes yours. _ Cloud computing is task-centric. Instead offocusing on the application andwhat it can do the focus is on what you need done and how the application can do itfor you. Traditional applications—wordprocessing spreadsheets email and soon—are becoming less important than thedocuments they create.PART 2 Understanding Cloud Computing _ Cloud computing is powerful. Connecting hundreds or thousands of computerstogether in a cloud creates a wealth of computing power impossible with a singledesktop PC. _ Cloud computing is accessible. Because data is stored in the cloud users caninstantly retrieve more information from multiple repositories. You’re not limited t o asingle source of data asyou are with a desktop PC. _ Cloud computing is intelligent. Withall the various data stored on thecomputers in a cloud data mining and analysis are necessary to access thatinformation in an intelligent manner. _ Cloud computing is programmable. Many of the tasks necessary with cloudcomputing must be automated. For example to protect theintegrity of the datainformation stored on a single computer in the cloud must be replicated on othercomputers in the cloud. If that one compu ter goes offline the cloud’sprogrammingautomatically redistributesthat computer’s data to a new computer in the cloud. All these definitions behind us what constitutes cloud computing in the realworld As you’ll learn throughout this book a raft of web-hostedInternet-accessibleGroup-collaborative applications are currently available with many more on the way.Perhaps the best and most popular examples of cloud computing applications todayare the Google family of applications—Google Docs amp SpreadsheetsGoogleCalendar Gmail Picasa and the like. All of these applications are hosted on Google’sservers are accessible to any user with an Internet connection and can be used forgroup collaboration from anywhere in the world. In short cloud computing enables a shift from the computer to the user fromapplications to tasks and from isolated data to datathat can be accessed fromanywhere and shared with anyone. The user no longer has to take on the task of datamanagement he doesn’t even have to remember where the data is. All that matters isthat the data is in the cloud and thus immediately available to that user and to otherauthorized users.From Collaboration to the Cloud: A Short History of Cloud Computing Cloud computing has as its antecedents bothclient/server computing andpeer-to-peer distributed computing. It’s all a matter of how centralizedstoragefacilitates collaboration and how multiple computers work together to increasecomputing power.Client/Server Computing: Centralized Applications and Storage In the antediluvian days of computing pre-1980 or so everything operated ontheclient/server model. All the software applications all the data and all the controlresided on huge mainframe computers otherwise known as servers. If a user wantedto access specific data or run a program he had to connect to the mainframe gainappropriate access and then do his business while essentially “renting” the programor data from the server. Users connected to the server via a computer terminal sometimes called aworkstation or client. This computer was sometimes called a dumb terminal because itdidn’t have a lot if any memory storage space or processing power. It was merelya device that connected the user to and enabled him to use the mainframe computer. Users accessed the mainframe only when granted permission and the informationtechnology IT staff weren’t in the habit of handing out access casually. Even on amainframe computer processing power is limited—and the IT staff were theguardians of that power. Access was not immediate nor could two users access thesame data at the same time. Beyond that users pretty much had to take whatever the IT staff gavethem—with no variations. Want to customize a reportto show only a subset of thenormal information Can’t do it. Want to create a new report to look at some new dataYou can’t do it although the IT staff can—but on their schedulewhich might beweeks from now. The fact is when multiple people are sharing a single computer even if thatcomputer is a huge mainframe you have to wait your turn. Need to rerun a financialreport No problem—if you don’t mind waiting until this afternoon ortomorrowmorning. There isn’t always immediate access in aclient/server environment andseldom is there immediate gratification. So the client/server model while providing similar centralized storage differedfrom cloud computing in that it did not have a user-centric focus with client/servercomputing all the control rested with the mainframe—and with the guardians of thatsingle computer. It was not a user-enabling environment.Peer-to-Peer Computing: Sharing Resources As you can imagine accessing a client/server system was kind of a “hurry up andwait” experience. The server part of the system also created a huge bottleneck. Allcommunications between computers had to go through the server first howeverinefficient that might be. The obvious need to connect one computer to another without first hitting theserver led to the development of peer-to-peer P2P computing. P2P computingdefines a network architecture in which each computer has equivalent capabilities andresponsibilities. This is in contrast to the traditionalclient/server network architecturein which one or more computers are dedicated to serving the others. This relationshipis sometimes characterized as a master/slave relationship with the central server asthe master and the client computer as the slave. P2P was an equalizing concept. In the P2P environment every computer is aclient and a serverthere are no masters and slaves. By recognizing all computers onthe network as peers P2P enables direct exchange of resources and services. There isno need for a central server because any computer can function in that capacity whencalled on to do so. P2P was also a decentralizing concept. Control is decentralized with allcomputers functioning as equals. Content is also dispersed among the various peercomputers. No centralized server is assigned to host the available resources andservices. Perhaps the most notable implementation of P2P computing is the Internet. Manyof today’s usersforget or never knew that the Internet was initially conceived underits original ARPAnet guise as a peer-to-peer system that would share computingresources across the United States. The various ARPAnet sites—and there weren’tmany of them—were connectedtogether not as clients and servers but as equals. The P2P nature of the early Internet was best exemplified by the Usenet enet which was created back in 1979 was anetwork of computers accessed viathe Internet each of which hosted the entire contents of the network. Messages werepropagated between the peer computers users connecting to any single Usenet serverhad access to all or substantially all the messages posted to each individualserver.Although the users’ connection to the U senet server was of the traditionalclient/server nature the relationship between the Usenet servers was definitelyP2P—and presaged the cloud computing of today. That said not every part of the Internet is P2P in nature. With thedevelopment ofthe World Wide Web came a shift away from P2P back to the client/server model. Onthe web each website is served up by a group of computers and sites’ visitors useclient software web browsers to access it. Almost all content is centralized allcontrol is centralized and the clients have no autonomy or control in the process.Distributed Computing: Providing More Computing Power One of the most important subsets of the P2P model is that ofdistributedcomputing where idle PCs across a network or across the Internet are tapped toprovide computing power for large processor-intensive projects. It’s a simpleconceptall about cycle sharing between multiple computers. Apersonal computer running full-out 24 hours a day 7 days a week is capableof tremendous computing power. Most people don’t use their computers 24/7however so a good portion of a computer’s resources go unused. Distributedcomputing uses those resources. When a computer is enlisted for a distributed computing project software isinstalled on the machine to run various processing activities during those periodswhenthe PC is typically unused. The results of that spare-time processing areperiodically uploaded to the distributed computing network and combined withsimilar results from other PCs in the project. The resultif enough computers areinvolved simulates the processing power of much larger mainframes andsupercomputers—which is necessary for some very large and complexcomputingprojects. For example genetic research requires vast amounts of computing power. Left totraditional means it might take years to solve essential mathematical problems. Byconnecting together thousands or millions of individual PCs more power is appliedto the problem and the results are obtained that much sooner. Distributed computing dates back to 1973 when multiple computers werenetworked together at the Xerox PARC labs and worm software was developed tocruise through the network looking for idle resources. A more practical application ofdistributed computing appeared in 1988 when researchers at the DEC DigitalEquipment Corporation System Research Center developed software that distributedthe work to factor large numbers among workstations within their laboratory. By 1990a group of about 100 users utilizing this software had factored a 100-digit number.By 1995 this same effort had been expanded to the web to factor a 130-digit number.It wasn’t long before distributed computing hit the Internet. The first majorInternet-based distributed computing project was launched in 1997which employed thousands of personal computers to crack encryption codes. Evenbigger was SETIhome launched in May 1999 which linked together millions ofindividual computers to search forintelligent life in outer space. Many distributedcomputing projects are conducted within large enterprises using traditional networkconnections to form the distributed computing network. Other larger projects utilizethe computers of everyday Internet users with the computing typically taking placeoffline and then uploaded once a day viatraditional consumer Internet connections.Understanding Cloud Architecture The key to cloud computing is the“cloud”—a massive network of servers or evenindividual PCs interconnected in a grid.These computers run in parallel combiningthe resources of each to generatesupercomputing-like po.。

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Implementation Issues of A Cloud Computing PlatformBo Peng,Bin Cui and Xiaoming LiDepartment of Computer Science and Technology,Peking University{pb,bin.cui,lxm}@AbstractCloud computing is Internet based system development in which large scalable computing resources are provided“as a service”over the Internet to users.The concept of cloud computing incorporates web infrastructure,software as a service(SaaS),Web2.0and other emerging technologies,and has attracted more and more attention from industry and research community.In this paper,we describe our experience and lessons learnt in construction of a cloud computing platform.Speciﬁcally,we design a GFS compatibleﬁle system with variable chunk size to facilitate massive data processing,and introduce some implementation enhancement on MapReduce to improve the system throughput.We also discuss some practical issues for system implementation.In association of the China web archive(Web InfoMall) which we have been accumulating since2001(now it contains over three billion Chinese web pages), this paper presents our attempt to implement a platform for a domain speciﬁc cloud computing service, with large scale web text mining as targeted application.And hopefully researchers besides our selves will beneﬁt from the cloud when it is ready.1IntroductionAs more facets of work and personal life move online and the Internet becomes a platform for virtual human society,a new paradigm of large-scale distributed computing has emerged.Web-based companies,such as Google and Amazon,have built web infrastructure to deal with the internet-scale data storage and computation. If we consider such infrastructure as a“virtual computer”,it demonstrates a possibility of new computing model, i.e.,centralize the data and computation on the“super computer”with unprecedented storage and computing capability,which can be viewed as a simplest form of cloud computing.More generally,the concept of cloud computing can incorporate various computer technologies,including web infrastructure,Web2.0and many other emerging technologies.People may have different perspectives from different views.For example,from the view of end-user,the cloud computing service moves the application software and operation system from desktops to the cloud side,which makes users be able to plug-in anytime from anywhere and utilize large scale storage and computing resources.On the other hand,the cloud computing service provider may focus on how to distribute and schedule the computer resources.Nevertheless,the storage and computing on massive data are the key technologies for a cloud computing infrastructure.Google has developed its infrastructure technologies for cloud computing in recent years,including Google File System(GFS)[8],MapReduce[7]and Bigtable[6].GFS is a scalable distributedﬁle system,which Copyright0000IEEE.Personal use of this material is permitted.However,permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists,or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Bulletin of the IEEE Computer Society Technical Committee on Data Engineering61emphasizes fault tolerance since it is designed to run on economically scalable but inevitably unreliable(due to its sheer scale)commodity hardware,and delivers high performance service to a large number of clients. Bigtable is a distributed storage system based on GFS for structured data management.It provides a huge three-dimensional mapping abstraction to applications,and has been successfully deployed in many Google products.MapReduce is a programming model with associated implementation for massive data processing. MapReduce provides an abstraction by deﬁning a“mapper”and a“reducer”.The“mapper”is applied to every input key/value pair to generate an arbitrary number of intermediate key/value pairs.The“reducer”is applied to all values associated with the same intermediate key to generate output key/value pairs.MapReduce is an easy-to-use programming model,and has sufﬁcient expression capability to support many real world algorithms and tasks.The MapReduce system can partition the input data,schedule the execution of program across a set of machines,handle machine failures,and manage the inter-machine communication.More recently,many similar systems have been developed.KosmosFS[3]is an open source GFS-Like system,which supports strict POSIX interface.Hadoop[2]is an active Java open source project.With the support from Yahoo,Hadoop has achieved great progress in these two years.It has been deployed in a large system with4,000nodes and used in many large scale data processing tasks.In Oct2007,Google and IBM launched“cloud computing initiative”programs for universities to promote the related teaching and research work on increasingly popular large-scale ter in July2008,HP, Intel and Yahoo launched a similar initiative to promote and develop cloud computing research and education. Such cloud computing projects can not only improve the parallel computing education,but also promote the research work such as Internet-scale data management,processing and scientiﬁc computation.Inspired by this trend and motivated by a need to upgrade our existing work,we have implemented a practical web infrastructure as cloud computing platform,which can be used to store large scale web data and provide high performance processing capability.In the last decade,our research and system development focus is on Web search and Web Mining,and we have developed and maintained two public web systems,i.e.,Tianwang Search Engine[4]and Web Archive system Web infomall[1]as shown in Figure1.(a)Tianwang(b)Web infomallFigure1:Search engine and Chines web archive developed at SEWM group of PKU During this period,we have accumulated more than50TB web data,built a PC cluster consisting of100+ PCs,and designed various web application softwares such as webpage text analysis and processing.With the increase of data size and computation workload in the system,we found the cloud computing technology is a promising approach to improve the scalability and productivity of the system for web services.Since2007,we62started to design and develop our web infrastructure system,named“Tplatform”,including GFS-likeﬁle system “TFS”[10]and MapReduce computing environment.We believe our practice of cloud computing platform implementation could be a good reference for researchers or engineers who are interested in this area.2TPlatform:A Cloud Computing PlatformIn this section,we brieﬂy introduce the implementation and components of our cloud computing platform, named“Tplatform”.Weﬁrst present the overview of the system,followed by the detailed system implementation and some practical issues.Figure2:The System Framework of TplatformFig2shows the overall system framework of the“Tplatform”,which consists of three layers,i.e.,PC cluster, infrastructure for cloud computing platform,and data processing application layer.The PC cluster layer provides the hardware and storage devices for large scale data processing.The application layer provides the services to users,where users can develop their own applications,such as Web data analysis,language processing,cluster and classiﬁcation,etc.The second layer is the main focus of our work,consisting ofﬁle system TFS,distributed data storage mechanism BigTable,and MapReduce programming model.The implementation of BigTable is similar to the approach presented in[6],and hence we omit detailed discussion here.2.1Implementation of File SystemTheﬁle system is the key component of the system to support massive data storage and management.The designed TFS is a scalable,distributedﬁle system,and each TFS cluster consists of a single master and multiple chunk servers and can be accessed by multiple client.632.1.1TFS ArchitectureIn TFS,ﬁles are divided into variable-size chunks.Each chunk is identiﬁed by an immutable and globally unique 64bit chunk handle assigned by the master at the time of chunk creation.Chunk servers store the chunks on the local disks and read/write chunk data speciﬁed by a chunk handle and byte range.For the data reliability, each chunk is replicated on multiple chunk servers.By default,we maintain three replicas in the system,though users can designate different replication levels for differentﬁles.The master maintains the metadata ofﬁle system,which includes the namespace,access control information, the mapping fromﬁles to chunks,and the current locations of chunks.It also controls system-wide activities such as garbage collection of orphaned chunks,and chunk migration between chunk servers.Each chunk server periodically communicates with the master in HeartBeat messages to report its state and retrieve the instructions.TFS client module is associated with each application by integrating theﬁle system API,which can commu-nicate with the master and chunkservers to read or write data on behalf of the application.Clients interact with the master for metadata operations,but all data-bearing communication goes directly to the chunkservers.The system is designed to minimize the master’s involvement inﬁle accessing operations.We do not provide the POSIX API.Besides providing the ordinary read and write operations,like GFS,we have also provided an atomic record appending operation so that multiple clients can append concurrently to aﬁle without extra synchronization among them.In the system implementation,we observe that the record appending operation is the key operation for system performance.We design our own system interaction mechanism which is different from GFS and yields better record appending performance.2.1.2Variable Chunk SizeIn GFS,aﬁle is divided intoﬁxed-size chunks(e.g.,64MB).When a client uses record appending operation to append data,the system checks whether appending the record to the last chunk of a certainﬁle may make the chunk overﬂowed,i.e.,exceed the maximum size.If so,it pads all the replica of the chunk to the maximum size, and informs the client that the operation should be continued on the new chunk.(Record appending is restricted to be at most one-fourth of the chunk size to keep worst case fragmentation at an acceptable level.)In case of write failure,this approach may lead to duplicated records and incomplete records.In our TFS design,the chunks of aﬁle are allowed to have variable sizes.With the proposed system in-teraction mechanism,this strategy makes the record appending operation more efﬁcient.Padding data,record fragments and record duplications are not necessary in our system.Although this approach brings some extra cost,e.g.,every data structure of chunk needs a chunk size attribute,the overall performance is signiﬁcantly improved,as the read and record appending operations are the dominating operations in our system and can beneﬁt from this design choice.2.1.3File OperationsWe have designed differentﬁle operations for TFS,such as read,record append and write.Since we allow variable chunk size in TFS,the operation strategy is different from that of GFS.Here we present the detailed implementation of read operation to show the difference of our approach.To read aﬁle,the client exchanges messages with the master,gets the locations of chunks it wants to read from,and then communicates with the chunk servers to retrieve the data.Since GFS uses theﬁxed chunk size, the client just needs to translate theﬁle name and byte offset into a chunk index within theﬁle,and sends the master a request containing theﬁle name and chunk index.The master replies with the corresponding chunk handle and locations of the replicas.The client then sends a request to one of the replicas,most likely the closest one.The request speciﬁes the chunk handle and a byte range within that chunk.Further reads of the same chunk do not require any more client-master interaction unless the cached information expires or theﬁle is reopened.64In our TFS system,the story is different due to the variable chunk size strategy.The client can not translate the byte offset into a chunk index directly.It has to know all the sizes of chunks in theﬁle before deciding which chunk should be read.Our solution is quite straightforward,when a client opens aﬁle using read mode,it gets all the chunks’information from the master,including chunk handle,chunk size and locations,and use these information to get the proper chunk.Although this strategy is determined by the fact of variable chunk size, its advantage is that the client only needs to communicate with the master once to read the wholeﬁle,which is much efﬁcient than GFS’original design.The disadvantage is that when a client has opened aﬁle for reading, later appended data by other clients is invisible to this client.But we believe this problem is negligible,as the majority of theﬁles in web applications are typically created and appended once,and read by data processing applications many times without modiﬁcations.If in any situation this problem becomes critical,it can be easily overcome by set an expired timestamp for the chunks’information and refresh it when invalid.The TFS demonstrates our effort to build an infrastructure for large scale data processing.Although our system has the similar assumptions and architectures as GFS,the key difference is that the chunk size is variable, which makes our system able to adopt different system interactions for record appending operation.Our record appending operation is based on chunk level,thus the aggregate record appending performance is no longer restricted by the network bandwidth of the chunk servers that store the last chunk of theﬁle.Our experimental evaluation shows that our approach signiﬁcantly improves the concurrent record appending performance for singleﬁle by25%.More results on TFS have been reported in[10].We believe the design can apply to other similar data processing infrastructures.2.2Implementation of MapReduceMapReduce system is another major component of the cloud computing platform,and has attracted more and more attentions recently[9,7,11].The architecture of our implementation is similar to Hadoop[2],which is a typical master-worker structure.There are three roles in the system:Master,Worker and User.Master is the central controller of the system,which is in charge of data partitioning,task scheduling,load balancing and fault tolerance processing.Worker runs the concrete tasks of data processing and computation.There exist many workers in the system,which fetch the tasks from Master,execute the tasks and communicate with each other for data er is the client of the system,implements the Map and Reduce functions for computation task,and controls theﬂow of computation.2.2.1Implementation EnhancementWe make three enhancements to improve the MapReduce performance in our system.First,we treat intermediate data transfer as an independent task.Every computation task includes map and reduce subtasks.In a typical implementation such as Hadoop,reduce task starts the intermediate data transfer,which fetches the data from all the machines conducting map tasks.This is an uncontrollable all-to-all communication,which may incur network congestion,and hence degrade the system performance.In our design,we split the transfer task from the reduce task,and propose a“Data transfer module”to execute and schedule the data transfer task independently. With appropriate scheduling algorithm,this method can reduce the probability of network congestion.Although this approach may aggravate the workload of Master when the number of transfer tasks is large,this problem can be alleviated by adjusting the granularity of transfer task and integrating data transfer tasks with the same source and target addresses.In practice,our new approach can signiﬁcantly improve the data transfer performance.Second,task scheduling is another concern on MapReduce system,which helps to commit resources be-tween a variety of tasks and schedule the order of task execution.To optimize the system resource utility,we adopt multi-level feedback queue scheduling algorithm in our design.Multiple queues are used to allocate the concurrent tasks,and each of them is assigned with a certain priority,which may vary for different tasks with respect to the resources requested.Our algorithm can dynamically adjust the priority of running task,which65balances the system workload and improves the overall throughput.The third improvement is on data serialization.In MapReduce framework,a computation task consists of four steps:map,partition,group and reduce.The data is read in by map operation,intermediate data is gener-ated and transferred in the system,andﬁnally the results are exported by reduce operation.There exist frequent data exchanges between memory and disk which are generally accomplished by data serialization.In our imple-mentation of MapReduce system,we observed that the simple native data type is frequently used in many data processing applications.Since memory buffer is widely used,most of the data already reside in the memory before they are de-serialized into a new data object.In other words,we should avoid expensive de-serialization operations which consume large volume of memory space and degrade the system performance.To alleviate this problem,we deﬁne the data type for key and value as void*pointer.If we want to de-serialize the data with native data type,a simple pointer assignment operation can replace the de-serialization operation,which is much more efﬁcient.With this optimization,we can also sort the data directly in the memory without data de-serialization.This mechanism can signiﬁcantly improve the MapReduce performance,although it introduces some cost overhead for buffer management.2.2.2Performance Evaluation on MapReduceDue to the lack of benchmark which can represent the typical applications,performance evaluation on MapRe-duce system is not a trivial task.Weﬁrst use PennySort as the simple benchmark.The result shows that the performance of intermediate data transfer in the shufﬂe phase is the bottle neck of the system,which actually motivated us to optimize the data transfer module in MapReduce.Furthermore,we also explore a real applica-tion for text mining,which gathers statistics of Chinese word frequency in webpages.We run the program on a 200GB Chinese Web collection.Map function analyzes the content of web page,and produces every individual Chinese word as the key value.Reduce function sums up all aggregated values and exports the frequencies.In our testbed with18nodes,the job was split into3385map tasks,30reduce tasks and101550data transfer tasks, the whole job was successfully completed in about10hours,which is very efﬁcient.2.3Practical Issues for System ImplementationThe data storage and computation capability are the major factors of the cloud computing platform,which determine how well the infrastructure can provide services to end users.We met some engineering and technical problems during the system implementation.Here we discuss some practical issues in our work.2.3.1System Design CriteriaIn the system design,our purpose is to develop a system which is scalable,robust,high-performance and easy to be maintained.However,some system design issues may be conﬂicted,which places us in a dilemma in many cases.Generally,we take three major criteria into consideration for system design:1)For a certain solution, what is bottleneck of the procedure which may degenerate the system performance?2)Which solution has better scalability andﬂexibility for future change?3)Since network bandwidth is the scarce resource of the system, how to fully utilize the network resource in the implementation?In the following,we present an example to show our considerations in the implementation.In the MapReduce system,fault tolerance can be conducted by either master or workers.Master takes the role of global controller,maintains the information of the whole system and can easily decide whether a failed task should be rerun,and when/where to be rerun.Workers only keep local information,and take charge of reporting the status of running tasks to Master.Our design combines the advantages of these two factors.The workers can rerun a failed task for a certain number of times,and are even allowed to skip some bad data records which cause the failure.This distributed strategy is more robust and scalable than centralized mechanism,i.e., only re-schedule failed tasks in the Master side.662.3.2Implementation of Inter-machine CommunicationSince the implementation of cloud computing platform is based on the PC cluster,how to design the inter-machine communication protocol is the key issue of programming in the distributed environment.The Remote Procedure Call(RPC)middle ware is a popular paradigm for implementing the client-server model of distributed computing,which is an inter-process communication technology that allows a computer program to cause a subroutine or procedure to execute on another computer in a PC cluster without the programmer explicitly coding the details for this remote interaction.In our system,all the services and heart-beat protocols are RPC calls.We exploit Internet Communications Engine(ICE),which is an object-oriented middleware that provides object-oriented RPC,to implement the RPC framework.Our approach performs very well under our system scale and can support asynchronous communication model.The network communication performance of our system with ICE is comparable to that of special asynchronous protocols with socket programming,which is much more complicated for implementation.2.3.3System Debug and DiagnosisDebug and Diagnosis in distributed environment is a big challenge for researchers and engineers.The overall system consists of various processes distributed in network,and these processes communicate each other to execute a complex task.Because of the concurrent communications in such system,many faults are generally not easy to be located,and hence can hardly be debugged.Therefore,we record complete system log in our system.In All the server and client sides,important software boundaries such as API and RPC interfaces are all logged.For example,log for RPC messages can be used to check integrality of protocol,log for data transfer can be used to validate the correctness of transfer.In addition,we record performance log for performance tuning. In our MapReduce system,log in client side records the details of data read-in time,write-out time of all tasks, time cost of sorting operation in reduce task,which are tuning factors of our system design.In our work,the recorded log not only helps us diagnose the problems in the programs,but also helps ﬁnd the performance bottleneck of the system,and hence we can improve system implementation accordingly. However,distributed debug and diagnosis are still low efﬁcient and labor consuming.We expect better tools and approaches to improve the effectiveness and efﬁciency of debug and diagnosis in large scale distributed system implementation.3ConclusionBased on our experience with Tplatform,we have discussed several practical issues in the implementation of a cloud computing platform following Google model.It is observed that while GFS/MapReduce/BigTable provides a great conceptual framework for the software core of a cloud and Hadoop stands for the most popular open source implementation,there are still many interesting implementation issues worth to explore.Three are identiﬁed in this paper.•The chunksize of aﬁle in GFS can be variable instead ofﬁxed.With careful implementation,this design decision delivers better performance for read and append operations.•The data transfer among participatory nodes in reduce stage can be made”schedulable”instead of”un-controlled”.The new mechanism provides opportunity for avoiding network congestions that degrade performance.•Data with native types can also be effectively serialized for data access in map and reduce functions,which presumably improves performance in some cases.67While Tplatform as a whole is still in progress,namely the implementation of BigTable is on going,the ﬁnished parts(TFS and MapReduce)are already useful.Several applications have shown the feasibility and advantages of our new implementation approaches.The source code of Tplatform is available from[5]. AcknowledgmentThis work was Supported by973Project No.2007CB310902,IBM2008SUR Grant for PKU,and National Natural Science foundation of China under Grant No.60603045and60873063.References[1]China Web InfoMall.,2008.[2]The Hadoop Project./,2008.[3]The KosmosFS Project./,2008.[4]Tianwang Search.,2008.[5]Source Code of Tplatform Implementation./˜webg/tplatform,2009.[6]F.Chang,J.Dean,S.Ghemawat,W.C.Hsieh,D.A.Wallach,M.Burrows,T.Chandra,A.Fikes,and R.E.Gruber.Bigtable:a distributed storage system for structured data.In OSDI’06:Proceedings of the7th USENIX Symposium on Operating Systems Design and Implementation,pages15–15,2006.[7]J.Dean and S.Ghemawat.Mapreduce:Simpliﬁed data processing on large clusters.In OSDI’04:Proceed-ings of the5th USENIX Symposium on Operating Systems Design and Implementation,pages137–150, 2004.[8]G.Sanjay,G.Howard,and L.Shun-Tak.The googleﬁle system.In Proceedings of the17th ACM Sympo-sium on Operating Systems Principles,pages29–43,2003.[9]H.Yang,A.Dasdan,R.Hsiao,and D.S.Parker.Map-reduce-merge:simpliﬁed relational data processingon large clusters.In SIGMOD’07:Proceedings of the2007ACM SIGMOD international conference on Management of data,pages1029–1040,2007.[10]Z.Yang,Q.Tu,K.Fan,L.Zhu,R.Chen,and B.Peng.Performance gain with variable chunk size ingfs-likeﬁle systems.In Journal of Computational Information Systems,pages1077–1084,2008.[11]M.Zaharia,A.Konwinski,A.D.Joseph,R.Katz,and I.Stoica.Improving mapreduce performance inheterogeneous environments.In OSDI’07:Proceedings of the8th USENIX Symposium on Operating Systems Design and Implementation,pages29–42,2007.68。

物联网的关键技术的研究和应用大学毕业论文外文文献翻译及原文

毕业设计（论文）外文文献翻译文献、资料中文题目：物联网的关键技术的研究和应用文献、资料英文题目：文献、资料来源：文献、资料发表（出版）日期：院（部）：专业：班级：姓名：学号：指导教师：翻译日期： 2017.02.14毕业设计（论文）译文及原稿译文题目：物联网的关键技术的研究和应用Research on Key Technology and Applications for Internet of 原稿题目：ThingsXian-Yi Chen1, 2, Zhi-Gang Jin3.[J].SciV erse Sciencedirect，原稿出处：2012，Physics Procedia 33：561-566.物联网的关键技术的研究和应用摘要物联网（IOT）已经在在世界各地的各个行业和政府以及被学术界被越来越多的关注。

本文就物联网的概念和物联网的体系结构进行了讨论。

并且对物联网的关键技术，包括射频识别技术、电子产品代码技术、无线个域网技术进行了分析。

数字农业的框架下也提出了基于物联网的应用。

1.1物联网物联网的概念是在1999年在MIT（麻省理工学院）的Auto-ID实验室首次提出它是指所有的物品为了实现智能识别和网络管理通过类似于RIFD（射频识别RFID）等的传感器设备连接到互联网。

其核心支持技术是无线传感器网络和射频识别技术。

物联网的概念是在2005年在国际电信联盟报告中提出的：物联网,由国际电信联盟（ITU）在突尼斯2005年11月17日的信息社会世界峰会(WSIS)中向全世界正式发布。

据报道,一切在任何地方和任何时间通过无线射频识别技术、无线传感器网络技术、智能嵌入式技术和纳米技术可以连接到对方。

由于没有统一的物联网的定义，它可以从以下技术角度来定义。

物联网是万物的网络，可以实现互连，随时有完整的意识，传输可靠，准确控制，智能处理和其他特征的支持技术,如微型电极、射频识别、无线传感器网络技术、智能嵌入技术,互联网技术,集成智能处理技术，纳米技术。

物联网中英文对照外文翻译文献

物联网中英文对照外文翻译文献一、引言物联网（Internet of Things，IoT）作为当今信息技术领域的热门话题，正在深刻地改变着我们的生活和工作方式。

它通过将各种物理设备与互联网连接，实现了设备之间的智能交互和数据共享，为人们带来了前所未有的便利和效率。

在这一领域，中英文对照的外文翻译文献对于推动技术的发展和交流具有重要的意义。

二、物联网的概念和特点（一）物联网的定义物联网是指通过各种信息传感设备，实时采集任何需要监控、连接、互动的物体或过程等各种需要的信息，与互联网结合形成的一个巨大网络。

其目的是实现物与物、人与物之间的智能化识别、定位、跟踪、监控和管理。

（二）物联网的特点1、全面感知通过各种传感器和智能设备，实现对物理世界的全面感知和数据采集。

2、可靠传输利用多种通信技术，确保数据的稳定、安全和快速传输。

3、智能处理运用大数据分析、人工智能等技术，对采集到的数据进行处理和分析，以实现智能化的决策和控制。

三、物联网的关键技术（一）传感器技术传感器是物联网获取信息的基础，能够将物理世界的各种信号转换为电信号。

（二）射频识别技术（RFID）通过无线电波实现对物体的自动识别和数据采集。

（三）无线通信技术包括 WiFi、蓝牙、Zigbee 等，为物联网设备之间的通信提供支持。

（四）云计算和大数据技术用于处理和存储海量的物联网数据，并从中挖掘有价值的信息。

四、物联网的应用领域（一）智能家居实现家庭设备的智能化控制和管理，提高生活的舒适性和便利性。

（二）智能交通优化交通流量，提高交通运输的安全性和效率。

（三）工业物联网提升工业生产的自动化水平和管理效率，降低成本。

（四）医疗物联网改善医疗服务质量，实现患者的远程监护和医疗资源的优化配置。

五、物联网中英文对照外文翻译文献的重要性（一）促进技术交流帮助不同国家和地区的研究人员和工程师更好地了解彼此的研究成果和技术进展。

（二）加速技术创新为国内的研究和开发提供新的思路和方法，推动物联网技术的创新发展。

外文文献及翻译_ Cloud Computing 云计算

本科毕业设计外文文献及译文文献、资料题目：Cloud Computing文献、资料来源：云计算概述（英文版）文献、资料发表（出版）日期：2009年5月院（部）：专业：班级：姓名：学号：指导教师：翻译日期：外文文献：Cloud Computing1. Cloud Computing at a Higher LevelIn many ways, cloud computing is simply a metaphor for the Internet, the increasing movement of compute and data resources onto the Web. But there’s a difference: cloud computing represents a new tipping point for the value of network computing. It delivers higher efficiency, massive scalability, and faster, easier software development. It’s about new programming models, new IT infrastructure, and the enabling of new business models.For those developers and enterprises who want to embrace cloud computing, Sun is developing critical technologies to deliver enterprise scale and systemic qualities to this new paradigm:(1) Interoperability — while most current clouds offer closed platforms and vendor lock-in, developers clamor for interoperability. Sun’s open-source product strategy and Java™ principles are focused on providing interoperability for large-scale computing resources. Think of the existing cloud “islands” merging into a new, interoperable “Intercloud” where applications can be moved to and operate across multiple platforms.(2) High-density horizontal computing —Sun is pioneering high-power-density compute-node architectures and extreme-scale Infiniband fabrics as part of our top-tier HPC deployments. This high-density technology is being incorporated into our large-scale cloud designs.(3)Data in the cloud — More than just compute utilities, cloud computing is increasingly about petascale data. Sun’s Open Storage products offer hybrid data servers with unprecedented efficiency and performance for the emerging data-intensive computing applications that will become a key part of the cloud.These technology bets are focused on driving more efficient large-scale cloud deployments that can provide the infrastructure for next-generation business opportunities: social networks, algorithmic trading, continuous risk analysis, and so on.2. Why Cloud Computing?(1)Clouds: Much More Than Cheap ComputingCloud computing brings a new level of efficiency and economy to delivering IT resources on demand — and in the process it opens up new business models and market opportunities.While many people think of current cloud computing offerings as purely “pay by the drink” compute platforms, they’re really a convergence of two major interdependent IT trends: IT Efficiency — Minimize costs where companies are converting their IT costs from capital expenses to operating expenses through technologies such as virtualization. Cloud computing begins as a way to improve infrastructure resource deployment and utilization, but fully exploiting this infrastructure eventually leads to a new application development model.Business Agility — Maximize return using IT as a competitive weapon through rapid time to market, integrated application stacks, instant machine image deployment, and petascale parallel programming. Cloud computing is embraced as a critical way to revolutionize time to service. But inevitably these services must be built on equally innovative rapid-deployment-infrastructure models.To be sure, these trends have existed in the IT industry for years. However, the recent emergence of massive network bandwidth and virtualization technologies has enabled this transformation to a new services-oriented infrastructure.Cloud computing enables IT organizations to increase hardware utilization rates dramatically, and to scale up to massive capacities in an instant — without constantly having to invest in new infrastructure, train new personnel, or license new software. It also creates new opportunities to build a better breed of network services, in less time, for less money.IT Efficiency on a Whole New ScaleCloud computing is all about efficiency. It provides a way to deploy and access everything from single systems to huge amounts of IT resources — on demand, in real time, at an affordable cost. It makes high-performance compute and high-capacity storage available to anyone with a credit card. And since the best cloud strategies build on concepts and tools that developers already know, clouds also have the potential to redefine the relationship between information technology and the developers and business units that depend on it.Reduce capital expenditures — Cloud computing makes it possible for companies to convert IT costs from capital expense to operating expense through technologies such as virtualization.Cut the cost of running a datacenter — Cloud computing improves infrastructure utilizationrates and streamlines resource management. For example, clouds allow for self-service provisioning through APIs, bringing a higher level of automation to the datacenter and reducing management costs.Eliminate over provisioning — Cloud computing provides scaling on demand, which, when combined with utility pricing, removes the need to overprovision to meet demand. With cloud computing, companies can scale up to massive capacities in an instant.For those who think cloud computing is just fluff, take a closer look at the cloud offerings that are already available. Major Internet providers , Google, and others are leveraging their infrastructure investments and “sharing” their large-scale economics. Already the bandwidth used by Amazon Web Services (AWS) exceeds that associated with their core e-tailing services. Forward-looking enterprises of all types —from Web 2.0 startups to global enterprises — are embracing cloud computing to reduce infrastructure costs.Faster, More Flexible ProgrammingCloud computing isn’t only about hardware —it’s also a programming revolution. Agile, easy-to-access, lightweight Web protocols —coupled with pervasive horizontally scaled architecture — can accelerate development cycles and time to market with new applications and services. New business functions are now just a script away.Accelerated cycles — The cloud computing model provides a faster, more efficient way to develop the new generation of applications and services. Faster development and testing cycles means businesses can accomplish in hours what used to take days, weeks, or months.Increase agility —Cloud computing accommodates change like no other model. For example, Animoto Productions, makers of a mashup tool that creates video from images and music, used cloud computing to scale up from 50 servers to 3,500 in just three days. Cloud computing can also provide a wider selection of more lightweight and agile development tools, simplifying and speeding up the development process.The immediate impact will be unprecedented flexibility in service creation and accelerated development cycles. But at the same time, development flexibility could become constrained by APIs if they’re not truly open. Cloud computing can usher in a new era of productivity for developers if they build on platforms that are designed to be federated rather than centralized. But there’s a major shift underway in programming culture and the languages that will be used inclouds.Today, the integrated, optimized, open-source Apache, MySQL, PHP/Perl/Python (AMP) stack is the preferred platform for building and deploying new Web applications and services. Cloud computing will be the catalyst for the adoption of an even newer stack of more lightweight, agile tools such as lighttpd, an open-source Web server; Hadoop, the free Java software framework that supports data-intensive distributed applications; and MogileFS, a file system that enables horizontal scaling of storage across any number of machines.(2)Compelling New Opportunities: The Cloud EcosystemBut cloud computing isn’t just about a proliferation of Xen image stacks on a restricted handful of infrastructure providers. It’s also about an emerging ecosyst em of complementary services that provide computing resources such as on-ramps for cloud abstraction, professional services to help in deployment, specialized application components such as distributed databases, and virtual private datacenters for the entire range of IT providers and consumers.These services span the range of customer requirements, from individual developers and small startups to large enterprises. And they continue to expand the levels of virtualization, a key architectural component of the cloud that offers ever-higher abstractions of underlying services.(3) How Did Cloud Computing Start?At a basic level, cloud computing is simply a means of delivering IT resources as services. Almost all IT resources can be delivered as a cloud service: applications, compute power, storage capacity, networking, programming tools, even communications services and collaboration tools.Cloud computing began as large-scale Internet service providers such as Google, Amazon, and others built out their infrastructure. An architecture emerged: massively scaled, horizontally distributed system resources, abstracted as virtual IT services and managed as continuously configured, pooled resources. This architectural model was immortalized by George Gilder in his Oc tober 2006 Wired magazine article titled “The Information Factories.” The server farms Gilder wrote about were architecturally similar to grid computing, but where grids are used for loosely coupled, technical computing applications, this new cloud model was being applied to Internet services.Both clouds and grids are built to scale horizontally very efficiently. Both are built to withstand failures of individual elements or nodes. Both are charged on a per-use basis. But whilegrids typically process batch jobs, with a defined start and end point, cloud services can be continuous. What’s more, clouds expand the types of resources available—file storage, databases, and Web services — and extend the applicability to Web and enterprise applications.At the same time, the concept of utility computing became a focus of IT design and operations. As Nick Carr observed in his book The Big Switch, computing services infrastructure was beginning to parallel the development of electricity as a utility. Wouldn’t it b e great if you could purchase compute resources, on demand, only paying for what you need, when you need it?For end users, cloud computing means there are no hardware acquisition costs, no software licenses or upgrades to manage, no new employees or consultants to hire, no facilities to lease, no capital costs of any kind —and no hidden costs. Just a metered, per-use rate or a fixed subscription fee. Use only what you want, pay only for what you use.Cloud computing actually takes the utility model to the next level. It’s a new and evolved form of utility computing in which many different types of resources (hardware, software, storage, communications, and so on) can be combined and recombined on the fly into the specific capabilities or services customers require. From CPU cycles for HPC projects to storage capacity for enterprise-grade backups to complete IDEs for software development, cloud computing can deliver virtually any IT capability, in real time. Under the circumstances it is easy to see that a broad range of organizations and individuals would like to purchase “computing” as a service, and those firms already building hyperscale distributed data centers would inevitably choose to begin offering this infrastructure as a service.(4)Harnessing Cloud ComputingSo how does an individual or a business take advantage of the cloud computing trend? It’s not just about loading machine images consisting of your entire software stack onto a public cloud like AWS — there are several different ways to exploit this infrastructure and explore the ecosystem of new business models.Use the CloudThe number and quality of public, commercially available cloud-based service offerings is growing fast. Using the cloud is often the best option for startups, research projects, Web 2.0 developers, or niche players who want a simple, low-cost way to “load and go.”If you’re an Internet startup today, you will be mandated by your investors to keep you IT spend to aminimum. This is certainly what the cloud is for.Leverage the CloudTypically, enterprises are using public clouds for specific functions or workloads. The cloud is an attractive alternative for:Development and testing — this is perhaps the easiest cloud use case for enterprises (not just startup developers). Why wait to order servers when you don’t even know if the project will pass the proof of concept?Functional offloading —you can use the cloud for specific workloads. For example, SmugMug does its image thumbnailing as a batch job in the cloud.Augmentation — Clouds give you a new option for handling peak load or anticipated spikes in demand for services. This is a very attractive option for enterprises, but also potentially one of the most difficult use cases. Success is dependent on the statefulness of the application and the interdependence with other datasets that may need to be replicated and load-balanced across the two sites.Experimenting — Why download demos of new software, and then install, license, and test it? In the future, software evaluation can be performed in the cloud, before licenses or support need to be purchased.Build the CloudMany large enterprises understand the economic benefits of cloud computing but want to ensure strict enforcement of security policies. So they’re experimenting fir st with “private” clouds, with a longer-term option of migrating mature enterprise applications to a cloud that’s able to deliver the right service levels.Other companies may simply want to build private clouds to take advantage of the economics of resource pools and standardize their development and deployment processes.Be the CloudThis category includes both cloud computing service providers and cloud aggregators —companies that offer multiple types of cloud services.As enterprises and service providers gain experience with the cloud architecture model and confidence in the security and access-control technologies that are available, many will decide to deploy externally facing cloud services. The phenomenal growth rates of some of the publiccloud offerings available today will no doubt accelerate the momentum. Amazon’s EC2 was introduced only two years ago and officially graduated from beta to general availability in October 2008.Cloud service providers can:Provide new routes to market for startups and Web 2.0 application developersOffer new value-added capabilities such as analyticsDerive a competitive edge through enterprise-level SLAsHelp enterprise customers develop their own cloudsIf you’re building large datacenters today, you should proba bly be thinking about whether you’re going to offer cloud services.(5)Public, Private, and Hybrid CloudsA company may choose to use a service provider’s cloud or build its own — but is it always all or nothing? Sun sees an opportunity to blend the advantages of the two primary options: Public clouds are run by third parties, and jobs from many different customers may be mixed together on the servers, storage systems, and other infrastructure within the cloud. End users don’t know who else’s job may be me running on the same server, network, or disk as their own jobs.Private clouds are a good option for companies dealing with data protection and service-level issues. Private clouds are on-demand infrastructure owned by a single customer who controls which applications run, and where. They own the server, network, and disk and can decide which users are allowed to use the infrastructure.But even those who feel compelled in the short term to build a private cloud will likely want to run applications both in privately owned infrastructure and in the public cloud space. This gives rise to the concept of a hybrid cloud.Hybrid clouds combine the public and private cloud models. You own parts and share other parts, though in a controlled way. Hybrid clouds offer the promise of on-demand, externally provisioned scale, but add the complexity of determining how to distribute applications across these different environments. While enterprises may be attracted to the promise of a hybrid cloud, this option, at least initially, will likely be reserved for simple stateless applications that require no complex databases or synchronization.3. Cloud Computing Defined(1)Cornerstone TechnologyWhile the basic technologies of cloud computing such as horizontally scaled, distributed compute nodes have been available for some time, virtualization — the abstraction of computer resources —is the cornerstone technology for all cloud architectures. With the ability to virtualize servers (behind a hypervisor-abstracted operating system), storage devices, desktops, and applications, a wide array of IT resources can now be allocated on demand.The dramatic growth in the ubiquitous availability of affordable high-bandwidth networking over the past several years is equally critical. What was available to only a small percentage of Internet users a decade ago is now offered to the majority of Internet users in North America, Europe, and Asia: high bandwidth, which allows massive compute and data resources to be accessed from the browser. Virtualized resources can truly be anywhere in the cloud — not just across gigabit datacenter LANs and WANs but also via broadband to remote programmers and end users.Additional enabling technologies for cloud computing can deliver IT capabilities on an absolutely unprecedented scale. Just a few examples:Sophisticated file systems such as ZFS can support virtually unlimited storage capacities, integration of the file system and volume management, snapshots and copy-on-write clones, on-line integrity checking, and repair.Patterns in architecture allow for accelerated development of superscale cloud architectures by providing repeatable solutions to common problems.New techniques for managing structured, unstructured, and semistructured data can provide radical improvements in data-intensive computing.Machine images can be instantly deployed, dramatically simplifying and accelerating resource allocation while increasing IT agility and responsiveness.(2)The Architectural Services Layers of Cloud ComputingWhile the first revolution of the Internet saw the three-tier (or n-tier) model emerge as a general architecture, the use of virtualization in clouds has created a new set of layers: applications, services, and infrastructure. These layers don’t just encapsu late on-demand resources; they also define a new application development model. And within each layer ofabstraction there are myriad business opportunities for defining services that can be offered on a pay-per-use basis.Software as a Service (SaaS)SaaS is at the highest layer and features a complete application offered as a service, on demand, via multitenancy —meaning a single instance of the software runs on the provider’s infrastructure and serves multiple client organizations.The most widely known example of SaaS is , but there are now many others, including the Google Apps offering of basic business services such as e-mail. Of course, ’s multitenant application has preceded the definition of cloud computing by a few years. On the other hand, like many other players in cloud computing, now operates at more than one cloud layer with its release of , a companion application development environment, or platform as a service.Platform as a Service (PaaS)The middle layer, or PaaS, is the encapsulation of a development environment abstraction and the packaging of a payload of services. The archetypal payload is a Xen image (part of Amazon Web Services) containing a basic Web stack (for example, a Linux distro, a Web server, and a programming environment such as Pearl or Ruby).PaaS offerings can provide for every phase of software development and testing, or they can be specialized around a particular area, such as content management.Commercial examples include Google App Engine, which serves applications on Google’s infrastructure. PaaS services such as these can provide a great deal of flexibility but may be constrained by the capabilities that are available through the provider.Infrastructure as a Service (IaaS)IaaS is at the lowest layer and is a means of delivering basic storage and compute capabilities as standardized services over the network. Servers, storage systems, switches, routers, and other systems are pooled (through virtualization technology, for example) to handle specific types of workloads — from batch processing to server/storage augmentation during peak loads.The best-known commercial example is Amazon Web Services, whose EC2 and S3 services offer bare-bones compute and storage services (respectively). Another example is Joyent whose main product is a line of virtualized servers which provide a highly scalable on-demandinfrastructure for running Web sites, including rich Web applications written in Ruby on Rails, PHP, Python, and Java.中文译文：云计算1.更高层次的云计算在很多情况下，云计算仅仅是互联网的一个隐喻，也就是网络上运算和数据资源日益增加的一个隐喻。

外文文献翻译大数据和云计算2017

大数据和云计算技术外文文献翻译(含：英文原文及中文译文)文献出处：Bryant R. The research of big data and cloud computing technology [J]. Information Systems, 2017, 3(5): 98-109英文原文The research of big data and cloud computing technologyBryant RoyAbstractThe rapid development of mobile Internet, Internet of Things, and cloud computing technologies has opened the prelude to the era of mobile cloud, and big data is increasingly attracting people's attention. The emergence of the Internet has shortened the distance between people, people, and the world. The entire world has become a "global village," and people have accessibility, information exchange, and collaborative work through the Internet. At the same time, with the rapid development of the Internet, the maturity and popularity of database technologies, and the emergence of high-memory, high-performance storage devices and storage media, the amount of data generated by humans in daily learning, living, and work is growing exponentially. The big data problem is generated under such a background. It has become a hot topic in scientific research and related industry circles. As one of the most cutting-edge topics in the field of information technology, it has attracted more andmore scholars to study the issue of big data.Keywords: big data; data analysis; cloud computing1 IntroductionBig data is an information resource that can reflect changes in the state and state of the physical world and the spiritual world. It has complexity, decision-making usefulness, high-speed growth, sparseness, and reproducibility. It generally has a variety of potential values. Based on the perspective of big data resources and management, big data is considered as an important resource that can support management decisions. Therefore, in order to effectively manage this resource and give full play to its potential value, it is necessary to study and solve such management problems as the acquisition, processing, application, definition of property rights, industrial development, and policy guarantee. Big data has the following characteristics:Complexity, as pointed out by many definitions, forms and characteristics of big data are extremely complex. In addition to the complexity of big data, the breadth of its sources, and the diversity of its morphological structure, the complexity of big data also manifests itself in uncertainties in its state changes and development methods. The usefulness of decision-making, big data itself is an objective large-scale data resources, and its direct function is limited. By analyzing, digging, and discovering the knowledge contained in it, it can provide decisionsupport for other practical applications that are difficult to provide with other resources. The value of big data is also reflected mainly through its decision-making usefulness. With rapid growth, this feature of big data resources is different from natural resources such as oil. The total stock of non-renewable natural resources will gradually decrease with the continuous exploitation of human beings. Big data, however, has rapid growth, that is, with continuous exploitation, big data resources will not only not decrease but will increase rapidly. The sparseness of value and the large amount of data in big data have brought many opportunities and brought many challenges. One of its main challenges is the low density of big data values. Although the number of big data resources is large, the useful value contained in it is sparse, which increases the difficulty of developing and utilizing big data resources.2 Big data processing flowData AcquisitionBig data, which originally meant a large quantity and variety of types, was extremely important for obtaining data information through various methods. Data collection is the most basic step in the process of big data processing. At present, commonly used data collection methods include RFID, data search and classification tools such as Google and other search engines, and bar code technology. And due to the emergence of mobile devices, such as the rapid spread of smart phones and tabletcomputers, a large amount of mobile software has been developed and applied, and social networks have become increasingly large. This has also accelerated the speed of information circulation and acquisition accuracy.Data Processing and IntegrationThe processing and integration of data is mainly to complete the proper processing of the collected data, cleaning and denoising, and further integrated storage. According to the foregoing, one of the characteristics of big data is diversity. This determines that the type and structure of data obtained through various channels are very complex, and brings great difficulties to subsequent data analysis and processing. Through the steps of data processing and integration, these complex structural data are first converted into a single or easy-to-handle structure, which lays a good foundation for future data analysis because not all information in these data is required. Therefore, these data must also be “de-noised” and cleaned to ensure da ta quality and reliability. The commonly used method is to design some data filters during the data processing process, and use the rule method of clustering or association analysis to pick out unwanted or erroneous outlier data and filter it out to prevent it from adversely affecting the final data result; These integrated data are integrated and stored. This is a very important step. If it is simply placed at random, it will affect the access to future data. It is easy to causedata access problems. Now the general solution is to The establishment of a special database for specific types of data, and the placement of these different types of data information, can effectively reduce the time for data query and access, and increase the speed of data extraction.Data AnalysisData analysis is the most central part of the overall big data processing process, because in the process of data analysis, the value of the data will be found. After the processing and integration of the previous step data, the resulting data becomes the original data for data analysis, and the data is further processed and analyzed according to the application requirements of the required data. The traditional methods of data processing analysis include data mining, machine learning, intelligent algorithms, and statistical analysis. These methods can no longer meet the needs of data analysis in the era of big data. (Google is the most advanced data analysis technology, Google as the Internet The most widely used company for big data, pioneered the concept of "cloud computing" in 2006. The application of various internal data is based on Google's own internal research and development of a series of cloud computing technologies.Data InterpretationFor the majority of users of data and information, the most concerned is not the analysis and processing of data, but the interpretationand presentation of the results of big data analysis. Therefore, in a complete data analysis process, the interpretation of data results is crucial. important. If the results of data analysis cannot be properly displayed, data users will be troubled and even mislead users. The traditional data display method is to download the output in text form or display the processing result on the user's personal computer. However, as the amount of data increases, the results of data analysis tend to be more complicated. The use of traditional data display methods is insufficient to meet the output requirements of data analysis results. Therefore, in order to increase the number of dataAccording to explanations and demonstration capabilities, most companies now introduce data visualization technology as the most powerful way to explain big data. By visualizing the results, you can visualize the data analysis results to the user, which is more convenient for users to understand and accept the results. Common visualization technologies include collection-based visualization technology, icon-based technology, image-based technology, pixel-oriented technology, and distributed technology.3 Big Data ChallengesBig Data Security and Privacy IssuesWith the development of big data, the sources and applications of data are becoming more and more extensive. When browsing the webfreely on the Internet, a series of browsing trails are left. When logging in to a related website on the Internet, you need to input personal important information, such as an ID card. Number, mobile number, address, etc. Cameras and sensors are everywhere to record personal behavior and location information. Through relevant data analysis, data experts can easily discover people's behavior habits and personal important information. If this information is used properly, it can help companies in related fields to understand the needs and habits of customers at any time, so that enterprises can adjust their production plans and achieve greater economic benefits. However, if these important information are stolen by bad people, security issues such as personal information and property will follow. In order to solve the problem of data privacy in the era of big data, academics and industry have come up with their own solutions. In addition, the speed of updating and changing data in the era of big data is accelerating, and general data privacy protection technologies are mostly based on static data protection, which brings new challenges to privacy protection. How to implement data privacy and security protection under complex and changing conditions will be one of the key directions for future big data research.Big Data Integration and ManagementLooking at the development process of big data, the sources and applications of big data are becoming more and more extensive. In orderto collect and collect data distributed in different data management systems, it is necessary to integrate and manage data. Although there are many methods for data integration and management, the traditional data storage methods can no longer meet the data processing requirements in the era of big data, which is facing new challenges. data storage. In the era of big data, one of the characteristics of big data is the diversity of data types. Data types are gradually transformed from traditional structured data into semi-structured and unstructured data. In addition, the sources of data are also gradually diversified. Most of the traditional data comes from a small number of military companies or research institutes' computer terminals; now, with the popularity of the Internet and mobile devices in the world, the storage of data is particularly important (by As can be seen in the previous article, traditional data storage methods are insufficient to meet the current data storage requirements. To deal with more and more massive data and increasingly complex data structures, many companies have started to develop distributed files suitable for the era of big data. System and distributed parallel database. In the data storage process, the data format of the transfer change is necessary, but also very critical and complex, which puts higher requirements on data storage systems.Big Data Ecological EnvironmentThe eco-environmental problem of big data involves firstly the issueof data resource management and sharing. This is an era of normalization and openness. The open structure of the Internet allows people to share all network resources in different corners of the earth at the same time. This has brought great convenience to scientific research. However, not all data can be shared unconditionally. Some data are protected by law because of their special value attributes and cannot be used unconditionally. Because the relevant legal measures are still not sound enough and lack sufficient data protection awareness, there is always the problem of data theft or ownership of data. This has both technical and legal issues. How to solve the problem of data sharing under the premise of protecting multiple interests will be an important challenge in the era of big data (In the era of big data, the production and application of data is not limited to a few special occasions, almost all areas, etc. Everyone can see the big data, so the data cross-cutting issues involved in these areas are inevitable. With the deepening of the influence of big data, big data analysis results will inevitably be on the national governance model, corporate decision-making, organization and Business processes, personal lifestyles, etc. will have a huge impact, and this mode of influence is worth further study in the future.中文译文大数据和云计算技术研究Bryant Roy摘要移动互联网、物联网和云计算技术的迅速发展，开启了移动云时代的序幕，大数据也越来越吸引人们的视线。

云计算毕业论文外文翻译

Cloud ComputingCloud computing is a service provided by way of a dynamic scalable virtualized computing model resources over the Internet. This mode offers available , convenient , on-demand network access into a shared pool of computing resources can be configured ( resources, including networks, servers, storage , applications, services ) , these resources can be quickly provided , just put it less administration , or with service providers rarely interact .It is calculated by the distribution of the large number of distributed computers, rather than the local computer or a remote server, running enterprise data centers and the Internet will be more similar. This allows companies to switch to the application of the resources required , according to demand access to a computer and storage systems. Like a single generator from the old model to a centralized power plant model . It means that computing power can also be used as a tradable commodity , like gas, water and electricity , as access to convenient , inexpensive. The biggest difference is that it is transmitted through the Internet.Cloud computing service characteristics and nature of clouds, water circulation on the Internet has a certainsimilarity , so the cloud is a very apt analogy.Cloud computing proposed for the development of computers played a role in making the network more widely based . In my opinion cloud computing has four significant advantages : Cloud computing provides the most reliable and secure data storage center , users do not have to worry about data loss, virus attacks and other problems ; cloud computing client devices require a minimum , is also the most convenience ; cloud computing data and applications can be easily shared between different devices ; cloud computing as we use the network provides almost infinite number of possibilities.But on the other hand there are also disadvantages two aspects : first, it is safe , because the cloud computing power and data in the cloud , how to ensure the security of customer data is a relatively important . Security has two aspects, one is the data is not lost , the general service providers will have the ability to solve backup , but also occurs occasionally lost ; Another is that your data will not leak , although this will take some measures to service providers , not to outsiders , such as hacker attacks to get data , but the problem is the service provider 's internal staff is great . The second is the network delay or interruption. Cloud computing generally remoteaccess via the network, although it is now improving speed quickly, and LAN but compared to the speed or delays , and if once a network outage , the service will not be able to access .云计算云计算是一种通过Internet以办事的方式提供动态可伸缩的虚拟化的资源的计算模式。

云计算技术的应用与发展趋势(英文中文双语版优质文档)

云计算技术的应用与发展趋势（英文中文双语版优质文档）With the continuous development of information technology, cloud computing technology has become an indispensable part of enterprise information construction. Cloud computing technology can help enterprises realize a series of functions such as resource sharing, data storage and processing, application development and deployment. This article will discuss from three aspects: the application of cloud computing technology, the advantages of cloud computing technology and the development trend of cloud computing technology.1. Application of Cloud Computing Technology1. Resource sharingCloud computing technology can bring together different resources to realize resource sharing. Enterprises can use cloud computing technology to share resources such as servers, storage devices, and network devices, so as to maximize the utilization of resources.2. Data storage and processingCloud computing technology can help enterprises store and process massive data. Through cloud computing technology, enterprises can store data in the cloud to realize remote access and backup of data. At the same time, cloud computing technology can also help enterprises analyze and process data and provide more accurate decision support.3. Application development and deploymentCloud computing technology can help enterprises develop and deploy applications faster and more conveniently. Through cloud computing technology, enterprises can deploy applications on the cloud to realize remote access and management of applications. At the same time, cloud computing technology can also provide a variety of development tools and development environment, which is convenient for enterprises to carry out application development.2. Advantages of cloud computing technology1. High flexibilityCloud computing technology can flexibly adjust the usage and allocation of resources according to the needs of enterprises, so as to realize the optimal utilization of resources. At the same time, cloud computing technology can also support elastic expansion and contraction, which is convenient for enterprises to cope with business peaks and valleys.2. High securityCloud computing technology can ensure the security of enterprise data through data encryption, identity authentication, access control and other means. At the same time, cloud computing technology can also provide a multi-level security protection system to prevent security risks such as hacker attacks and data leakage.3. Cost-effectiveCompared with the traditional IT construction model, the cost of cloud computing technology is lower. Through cloud computing technology, enterprises can avoid large-scale hardware investment and maintenance costs, and save enterprise R&D and operating expenses.4. Convenient managementCloud computing technology can help enterprises achieve unified resource management and monitoring. Through cloud computing technology, enterprises can centrally manage resources such as multiple servers, storage devices, and network devices, which is convenient for enterprises to carry out unified monitoring and management.5. Strong scalabilityCloud computing technology can quickly increase or decrease the usage and configuration of resources according to the needs of enterprises, so as to realize the rapid expansion and contraction of business. At the same time, cloud computing technology can also provide a variety of expansion methods, such as horizontal expansion, vertical expansion, etc., to facilitate enterprises to expand their business on demand.3. The development trend of cloud computing technology1. The advent of the multi-cloud eraWith the development of cloud computing technology, the multi-cloud era has arrived. Enterprises can choose different cloud platforms and deploy services on multiple clouds to achieve high availability and elastic expansion of services.2. Combination of artificial intelligence and cloud computingArtificial intelligence is one of the current hot technologies, and cloud computing technology can also provide better support for the development of artificial intelligence. Cloud computing technology can provide high-performance computing resources and storage resources, providing better conditions for the training and deployment of artificial intelligence.3. The Rise of Edge ComputingEdge computing refers to the deployment of computing resources and storage resources at the edge of the network to provide faster and more convenient computing and storage services. With the development of the Internet of Things and the popularization of 5G networks, edge computing will become an important expansion direction of cloud computing technology.4. Guarantee of security and privacyWith the widespread application of cloud computing technology, data security and privacy protection have become important issues facing cloud computing technology. In the future, cloud computing technology will pay more attention to security measures such as data encryption, identity authentication and access control to ensure the security and privacy of corporate and personal data.To sum up, cloud computing technology has become an indispensable part of enterprise information construction. Through cloud computing technology, enterprises can realize a series of functions such as resource sharing, data storage and processing, application development and deployment. At the same time, cloud computing technology also has the advantages of high flexibility, high security, high cost-effectiveness, convenient management and strong scalability. In the future, with the multi-cloud era, the combination of artificial intelligence and cloud computing, the rise of edge computing, and the protection of security and privacy, cloud computing technology will continue to enhance its importance and application value in enterprise information construction.随着信息技术的不断发展，云计算技术已经成为企业信息化建设中不可或缺的一部分。

物联网技术的应用及发展研究最新外文文献翻译

文献出处:Marisa D. The application and development of the Internet of things technology [J]. Internet Computing, IEEE, 2015, 12(5): 44-55.原文The application and development of the Internet of things technologyMarisa DAbstractInternet of things is considered through monitoring, analysis and control of network information technology, the extension of human perception of control ability has huge potential. Iot research work has been carried out. A lot of Iot demonstration system was also developed, and has made remarkable application effect. But at the same time, the current development of the Internet of things is also facing some fundamental problems: the Internet of things has what special requirements must be met? What phase are you in the Internet of things technology? Where is the development direction of Internet of things? It is worthwhile to explore these issues. This paper reviews the development of the Internet, and according to the experience of the development of the Internet, analyzes the present situation of Internet of things and Internet of things present in the "content - machine connected to the local small-scale network stage, its development direction should be connected to open net of numerous small" net ", namely the "Internet of things". Based on this idea, called WInternet Iot design, and introduces the overall architecture, working mode and protocol system, and also discusses the several other issues worthy of further study. Keywords: Internet of things; Pipeline agreement; Cloud calculation; Technology application1 IntroductionIn recent years, the development of the Internet of things has been attached great importance to, academia, industry, the government to give great attention to the development of the Internet of things. Internet of things is considered can connect hundreds of millions of physical world objects, through monitoring, analysis and control of network information technology, the extension of human perception control ability has huge potential. Iot research work has been carried out. A lot of Iotdemonstration system was also developed, and has made remarkable application effect. But at the same time, the current development of the Internet of things is also facing some problems, especially all kinds of Internet of things generally are connected by "-" in the form of "network", although the implements of all kinds of physical objects in the local scope - machine is linked together, but different "net" resource sharing between the perception and control equipment. And because of the existing "- machine connected to the network is generally based on the special agreement, adapt to the need of the professional custom, cause a physical network is not open, hard to connectivity. To realize all kinds of network connectivity should be a Iot of development trend.2 Internet development history and experience2.1 Electronic equipment network systemsIn the 19th century to early 20th century, electronic equipment network of prototype has emerged. As the time of the telephone network, cable network, and other various types is private network system. Now in retrospect, these networks have been gradually replaced by the Internet; its reason is worth thinking about. Analysis of the network system can be found early, they generally have the following features: (1) Vertical integration, tightly coupledThe network system hardware, software and operation of the upper application mostly belong to an owner. Most of the various components of the integration in the network system is independently by the owner internal personnel, network in each part of the tightly coupled system.(2) The proprietary protocols to exchangeIn the network system of internal communication protocol is often according to the specific needs of each owner, in order to better the optimization and use of all kinds of equipment components are designed. Different owners of intellectual property rights and interests protection often will deal core part try to conceal, difficult to communication between different network systems. This method of "vertical integration, proprietary protocols" to satisfy the various network system of the optimization of resources and interests of the owner to protect specific needs, butalso directly led to the early electronic equipment network problems.(3) Resource sharing difficultBecause every electronic device network system is generally adopts the way of "vertical integration" structure, the network system in all kinds of electronic equipment and software are also often can only be used for the network users of the system. For example in the early days of the telephone network system, multiple phone companies have independent laid their phone lines, set up relevant telephone switching equipment, a relatively independent telephone network. Different lines and equipment cannot be Shared between the telephone network, caused the repeat purchase, resource sharing difficult.(4) Function to replicateAnother problem is that in the different network system to repeat the same or similar functions, such as the telephone network signaling in the instruction and the signal coding implementation. Features to replicate directly lead to two results: one is each owners are required for the design and implementation of general agreement and equipment, but due to the limitation of the technical strength of a single owner, will inevitably extend network independently design and development time; Second, under the limit of time and personnel, the realization of function module final quality more or less is not ideal. If different owners to cooperation, complementary advantages, functional modules will not only greatly shorten the development time, its quality will improve the quality and technology evolution speed will also increase.3 Internet of things present situation and the development direction3.1 The development of Internet of thingsIot technology emerges in various fields has also been a high degree of attention, many of the Internet of things application demonstration is put forward and the construction, especially in environmental monitoring, traffic control, disaster emergency, etc. The application of these systems has also made certain achievements. But at the same time, we can also see the current before the development of the Internet of things is with the Internet electronic networks have similar features, especially the "vertical integration" and "special deal". Currently, many of Iot systemare to solve the problem of specific requirements of a certain area or region, independent each other. Set up in the process of sensing equipment, software module, communication formats tend to be based on specific requirements for customization. Caused by agreement custom complex network connection between works, although perception control equipment resources abundant, but it is share difficulties, such as in the current a lot of video surveillance network, while the erection of all kinds of cameras everywhere, but its share is very difficult.3.2 Development direction of Internet of thingsFrom the development history of the Internet, we believe that the current development of the Internet of things was still in the "machine" of the "net" phase. This network connects many physical objects, can communicate with each other, data exchange, and implement all kinds of monitoring and control functions. Most of these networks for specific needs, using proprietary protocols, solve the problems of the current focus on each network owners. But at the same time, also can see, these of the “net” have a resource sharing and the needs of each other."Machine" of the "network" become connected to many of the "net" "open net" should be the development trend of the Internet of things. This trend is also our experience on the development course from the Internet.3.3 The design requirements of Internet of thingsMentioned before the Internet of things, it is using electronic technology to the physical world of awareness and control network. This has also led to the Internet of things with the traditional numerical computing systems and the Internet data transmission network system has different characteristics and requirements.(1) Ensure real-time performanceThe numerical simulation of numerical calculation, Internet of things different from traditional problem itself may not be directly brought about changes in the physical world. But the errors of a control instruction in the Internet of things or delay a disaster may directly result in physical space. In smart grid, for example, if an error control instruction is to control equipment in the grid, small causes energy waste, is can cause paralysis of the grid. The error here includes both the wrong instruction,also including the correct instruction at the wrong time to control equipment. In other words, the real time in the Internet of things than the traditional Internet and numerical calculation system has a higher request. The design of the Internet of things should be as guarantee for real-time important consideration.(2) Privacy promiseThe emergence of the Internet of things technology makes the collection of information easier. Perception of physical space object will more or less involve in the privacy of all kinds of people. Iot will cover these private data is connected to the network, it is possible to make these data are all types of users to access remotely. How to safeguard the privacy of data is not abused and theft, this is the Internet of things another design factors must be considered.(3) Calculation to the nearsIn the Internet of things because of the continuous perception of the physical world, the amount of data and therefore is great. Under the traditional centralized data processing for the Internet of things of huge amounts of data may no longer apply. Illegal vehicle tracking, for example, found accident vehicles, such as somewhere we hope in a wider range of the car to track. One option is to all video monitoring data set to the data center. But the time delay of the data set itself will be longer, to the network bandwidth requirement is high. This scheme is difficult. To ensure real-time performance, but also to save resources, it is better near the camera video data analysis and calculation, the identification of license plate and movement track, avoid the time delay of data transmission and network bandwidth, so as to improve timeliness and network efficiency. Similarly, in the field of smart grid wide-area control, similar problems also exist, all the analyses focused on monitoring data to the data center, and then send the result to the remote, the optical signal transmission time needed for this process is likely to exceed system control limit is allowed. In this case, the calculation to the nearest has become a necessity.译文物联网技术的应用及发展研究Marisa D摘要物联网被认为是通过信息技术进行监测、分析和控制的网络，在延伸人类的感知控制能力方面潜力巨大。

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文献信息：文献标题：Integration of Cloud Computing with Internet of Things: Challenges and Open Issues（云计算与物联网的集成：挑战与开放问题）国外作者：HF Atlam等人文献出处：《IEEE International Conference on Internet of Things》, 2017字数统计：英文4176单词，23870字符；中文7457汉字外文文献：Integration of Cloud Computing with Internet of Things:Challenges and Open IssuesAbstract The Internet of Things (IoT) is becoming the next Internet-related revolution. It allows billions of devices to be connected and communicate with each other to share information that improves the quality of our daily lives. On the other hand, Cloud Computing provides on-demand, convenient and scalable network access which makes it possible to share computing resources; indeed, this, in turn, enables dynamic data integration from various data sources. There are many issues standing in the way of the successful implementation of both Cloud and IoT. The integration of Cloud Computing with the IoT is the most effective way on which to overcome these issues. The vast number of resources available on the Cloud can be extremely beneficial for the IoT, while the Cloud can gain more publicity to improve its limitations with real world objects in a more dynamic and distributed manner. This paper provides an overview of the integration of the Cloud into the IoT by highlighting the integration benefits and implementation challenges. Discussion will also focus on the architecture of the resultant Cloud-based IoT paradigm and its new applications scenarios. Finally, open issues and future research directions are also suggested.Keywords: Cloud Computing, Internet of Things, Cloud based IoT, Integration.I.INTRODUCTIONIt is important to explore the common features of the technologies involved in the field of computing. Indeed, this is certainly the case with Cloud Computing and the Internet of Things (IoT) – two paradigms which share many common features. The integration of these numerous concepts may facilitate and improve these technologies. Cloud computing has altered the way in which technologies can be accessed, managed and delivered. It is widely agreed that Cloud computing can be used for utility services in the future. Although many consider Cloud computing to be a new technology, it has, in actual fact, been involved in and encompassed various technologies such as grid, utility computing virtualisation, networking and software services. Cloud computing provides services which make it possible to share computing resources across the Internet. As such, it is not surprising that the origins of Cloud technologies lie in grid, utility computing virtualisation, networking and software services, as well as distributed computing, and parallel computing. On the other hand, the IoT can be considered both a dynamic and global networked infrastructure that manages self-configuring objects in a highly intelligent way. The IoT is moving towards a phase where all items around us will be connected to the Internet and will have the ability to interact with minimum human effort. The IoT normally includes a number of objects with limited storage and computing capacity. It could well be said that Cloud computing and the IoT will be the future of the Internet and next-generation technologies. However, Cloud services are dependent on service providers which are extremely interoperable, while IoT technologies are based on diversity rather than interoperability.This paper provides an overview of the integration of Cloud Computing into the IoT; this involves an examination of the benefits resulting from the integration process and the implementation challenges encountered. Open issues and research directions are also discussed. The remainder of the paper is organised as follows: Section II provides the basic concepts of Cloud computing, IoT, and Cloud-based IoT; SectionIII discusses the benefits of integrating the IoT into the Cloud; Could-based IoT Architecture is presented in section IV; Section V illustrates different Cloud-based IoT applications scenarios. Following this, the challenges facing Cloud-based IoT integration and open research directions are discussed in Section VI and Section VII respectively, before Section VIII concludes the paper.II.BASIC CONCEPTSThis section reviews the basic concepts of Cloud Computing, the IoT, and Cloud-based IoT.1.Cloud ComputingThere exist a number of proposed definitions for Cloud computing, although the most widely agreed upon seems be that put forth by the National Institute of Standards and Technology (NIST). Indeed, the NIST has defined Cloud computing as "a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction".As stated in this definition, Cloud computing comprises four types of deployment models, three different service models, and five essential characteristics.Cloud computing deployment models are most commonly classified as belonging to the public Cloud, where resources are made available to consumers over the Internet. Public Clouds are generally owned by a profitable organisation (e.g. Amazon EC2). Conversely, the infrastructure of a private Cloud is commonly provided by a single organisation to serve the particular purposes of its users. The private Cloud offers a secure environment and a higher level of control (Microsoft Private Cloud). Hybrid Clouds are a mixture of private and public Clouds. This choice is provided for consumers as it makes it possible to overcome some of the limitations of each model. In contrast, a community Cloud is a Cloud infrastructure which is delivered to a group of users by a number of organisations which share the same need.In order to allow consumers to choose the service that suits them, services inCloud computing are provided at three different levels, namely: the Software as a Service (SaaS) model, where software is delivered through the Internet to users (e.g. GoogleApps); the Platform as a Service (PaaS) model, which offers a higher level of integrated environment that can build, test, and deploy specific software (e.g. Microsoft Azure); and finally, with the Infrastructure as a Service (IaaS) model, infrastructure such as storage, hardware and servers are delivered as a service (e.g. Amazon Web Services).2.Internet of ThingsThe IoT represents a modern approach where boundaries between real and digital domains are progressively eliminated by consistently changing every physical device to a smart alternative ready to provide smart services. All things in the IoT (smart devices, sensors, etc.) have their own identity. They are combined to form the communication network and will become actively participating objects. These objects include not only daily usable electronic devices, but also things like food, clothing, materials, parts, and subassemblies; commodities and luxury items; monuments and landmarks; and various forms of commerce and culture. In addition, these objects are able to create requests and alter their states. Thus, all IoT devices can be monitored, tracked and counted, which significantly decreases waste, loss, and cost.The concept of the IoT was first mentioned by Kevin Ashton in 1999, when he stated that “The Internet of Things has the potential to change the world, just as the Internet did. Maybe even more so”. Later, the IoT was formally presented by the International Telecommunication Union (ITU) in 2005. A great many definitions of the IoT have been put forth by numerous organisations and researchers. According to the ITU (2012), the IoT is “a global infrastructure for the Information Society, enabling advanced services by interconnecting (physical and virtual) things based on, existing and evolving, interoperable information and communication technologies”. The IoT introduces a variety of opportunities and applications. However, it faces many challenges which could potentially hinder its successful implementation, such as data storage, heterogeneous resource-constrained, scalability, Things, variable geospatial deployment, and energy efficiency.3.Cloud-Based Internet of ThingsThe IoT and Cloud computing are both rapidly developing services, and have their own unique characteristics. On the one hand, the IoT approach is based on smart devices which intercommunicate in a global network and dynamic infrastructure. It enables ubiquitous computing scenarios. The IoT is typically characterised by widely-distributed devices with limited processing capabilities and storage. These devices encounter issues regarding performance, reliability, privacy, and security. On the other hand, Cloud computing comprises a massive network with unlimited storage capabilities and computation power. Furthermore, it provides a flexible, robust environment which allows for dynamic data integration from various data sources. Cloud computing has partially resolved most of the IoT issues. Indeed, the IoT and Cloud are two comparatively challenging technologies, and are being combined in order to change the current and future environment of internetworking services.The Cloud-based Internet of Things is a platform which allows for the smart usage of applications, information, and infrastructure in a cost-effective way. While the IoT and Cloud computing are different from each other, their features are almost complementary, as shown in TABLE 1. This complementarity is the primary reason why many researchers have proposed their integration.TABLE 1. COMPARISON OF THE IOT WITH CLOUD COMPUTINGIII.BENEFITS OF INTEGRATING IOT WITH CLOUDSince the IoT suffers from limited capabilities in terms of processing power and storage, it must also contend with issues such as performance, security, privacy, reliability. The integration of the IoT into the Cloud is certainly the best way toovercome most of these issues. The Cloud can even benefit from the IoT by expanding its limits with real world objects in a more dynamic and distributed way, and providing new services for billions of devices in different real life scenarios. In addition, the Cloud provides simplicity of use and reduces the cost of the usage of applications and services for end-users. The Cloud also simplifies the flow of the IoT data gathering and processing, and provides quick, low-cost installation and integration for complex data processing and deployment. The benefits of integrating IoT into Cloud are discussed in this section as follows.municationApplication and data sharing are two significant features of the Cloud-based IoT paradigm. Ubiquitous applications can be transmitted through the IoT, whilst automation can be utilised to facilitate low-cost data distribution and collection. The Cloud is an effective and economical solution which can be used to connect, manage, and track anything by using built-in apps and customised portals. The availability of fast systems facilitates dynamic monitoring and remote objects control, as well as data real-time access. It is worth declaring that, although the Cloud can greatly develop and facilitate the IoT interconnection, it still has weaknesses in certain areas. Thus, practical restrictions can appear when an enormous amount of data needs to be transferred from the Internet to the Cloud.2.StorageAs the IoT can be used on billions of devices, it comprises a huge number of information sources, which generate an enormous amount of semi-structured or non-structured data. This is known as Big Data, and has three characteristics: variety (e.g. data types), velocity (e.g. data generation frequency), and volume (e.g. data size). The Cloud is considered to be one of the most cost-effective and suitable solutions when it comes to dealing with the enormous amount of data created by the IoT. Moreover, it produces new chances for data integration, aggregation, and sharing with third parties.3.Processing capabilitiesIoT devices are characterised by limited processing capabilities which preventon-site and complex data processing. Instead, gathered data is transferred to nodes that have high capabilities; indeed, it is here that aggregation and processing are accomplished. However, achieving scalability remains a challenge without an appropriate underlying infrastructure. Offering a solution, the Cloud provides unlimited virtual processing capabilities and an on-demand usage model. Predictive algorithms and data-driven decisions making can be integrated into the IoT in order to increase revenue and reduce risks at a lower cost.4.ScopeWith billions of users communicating with one another together and a variety of information being collected, the world is quickly moving towards the Internet of Everything (IoE) realm - a network of networks with billions of things that generate new chances and risks. The Cloud-based IoT approach provides new applications and services based on the expansion of the Cloud through the IoT objects, which in turn allows the Cloud to work with a number of new real world scenarios, and leads to the emergence of new services.5.New abilitiesThe IoT is characterised by the heterogeneity of its devices, protocols, and technologies. Hence, reliability, scalability, interoperability, security, availability and efficiency can be very hard to achieve. Integrating IoT into the Cloud resolves most of these issues. It provides other features such as ease- of-use and ease-of-access, with low deployment costs.6.New ModelsCloud-based IoT integration empowers new scenarios for smart objects, applications, and services. Some of the new models are listed as follows:•SaaS (Sensing as a Service), which allows access to sensor data;•EaaS (Ethernet as a Service), the main role of which is to provide ubiquitous connectivity to control remote devices;•SAaaS (Sensing and Actuation as a Service), which provides control logics automatically;•IPMaaS (Identity and Policy Management as a Service), which provides access to policy and identity management;•DBaaS (Database as a Service), which provides ubiquitous database management;•SEaaS (Sensor Event as a Service), which dispatches messaging services that are generated by sensor events;•SenaaS (Sensor as a Service), which provides management for remote sensors;•DaaS (Data as a Service), which provides ubiquitous access to any type of data.IV.CLOUD-BASED IOT ARCHITECTUREAccording to a number of previous studies, the well-known IoT architecture is typically divided into three different layers: application, perception and network layer. Most assume that the network layer is the Cloud layer, which realises the Cloud-based IoT architecture, as depicted in Fig. 1.Fig. 1. Cloud-based IoT architectureThe perception layer is used to identify objects and gather data, which is collected from the surrounding environment. In contrast, the main objective of the network layer is to transfer the collected data to the Internet/Cloud. Finally, the application layer provides the interface to different services.V.CLOUD-BASED IOT APPLICATIONSThe Cloud-based IoT approach has introduced a number of applications and smart services, which have affected end users’ daily lives. TABLE 2 presents a brief discussion of certain applications which have been improved by the Cloud-based IoT paradigm.TABLE 2. CLOUD-BASED IOT APPLICATIONSVI.CHALLENGES FACING CLOUD-BASED IOT INTEGRATION There are many challenges which could potentially prevent the successful integration of the Cloud-based IoT paradigm. These challenges include:1.Security and privacyCloud-based IoT makes it possible to transport data from the real world to theCloud. Indeed, one particularly important issues which has not yet been resolved is how to provide appropriate authorisation rules and policies while ensuring that only authorised users have access to the sensitive data; this is crucial when it comes to preserving users’ privacy, and particularly when data integrity must be guaranteed. In addition, when critical IoT applications move into the Cloud, issues arise because of the lack of trust in the service provider, information regarding service level agreements (SLAs), and the physical location of data. Sensitive information leakage can also occur due to the multi-tenancy. Moreover, public key cryptography cannot be applied to all layers because of the processing power constraints imposed by IoT objects. New challenges also require specific attention; for example, the distributed system is exposed to number of possible attacks, such as SQL injection, session riding, cross- site scripting, and side-channel. Moreover, important vulnerabilities, including session hijacking and virtual machine escape are also problematic.2.HeterogeneityOne particularly important challenge faced by the Cloud- based IoT approach is related to the extensive heterogeneity of devices, platforms, operating systems, and services that exist and might be used for new or developed applications. Cloud platforms suffer from heterogeneity issues; for instance, Cloud services generally come with proprietary interfaces, thus allowing for resource integration based on specific providers. In addition, the heterogeneity challenge can be exacerbated when end-users adopt multi-Cloud approaches, and thus services will depend on multiple providers to improve application performance and resilience.3.Big dataWith many predicting that Big Data will reach 50 billion IoT devices by 2020, it is important to pay more attention to the transportation, access, storage and processing of the enormous amount of data which will be produced. Indeed, given recent technological developments, it is clear that the IoT will be one of the core sources of big data, and that the Cloud can facilitate the storage of this data for a long period of time, in addition to subjecting it to complex analysis. Handling the huge amount of data produced is a significant issue, as the application’s whole performance is heavilyreliant on the properties of this data management service. Finding a perfect data management solution which will allow the Cloud to manage massive amounts of data is still a big issue. Furthermore, data integrity is a vital element, not only because of its effect on the service’s quality, but also because of security and privacy issues, the majority of which relate to outsourced data.4.PerformanceTransferring the huge amount of data created from IoT devices to the Cloud requires high bandwidth. As a result, the key issue is obtaining adequate network performance in order to transfer data to Cloud environments; indeed, this is because broadband growth is not keeping pace with storage and computation evolution. In a number of scenarios, services and data provision should be achieved with high reactivity. This is because timeliness might be affected by unpredictable matters and real-time applications are very sensitive to performance efficiency.5.Legal aspectsLegal aspects have been very significant in recent research concerning certain applications. For instance, service providers must adapt to various international regulations. On the other hand, users should give donations in order to contribute to data collection.6.MonitoringMonitoring is a primary action in Cloud Computing when it comes to performance, managing resources, capacity planning, security, SLAs, and for troubleshooting. As a result, the Cloud-based IoT approach inherits the same monitoring demands from the Cloud, although there are still some related challenges that are impacted by velocity, volume, and variety characteristics of the IoT.rge scaleThe Cloud-based IoT paradigm makes it possible to design new applications that aim to integrate and analyse data coming from the real world into IoT objects. This requires interacting with billions of devices which are distributed throughout many areas. The large scale of the resulting systems raises many new issues that are difficult to overcome. For instance, achieving computational capability and storage capacityrequirements is becoming difficult. Moreover, the monitoring process has made the distribution of the IoT devices more difficult, as IoT devices have to face connectivity issues and latency dynamics.VII.OPEN ISSUES AND RESEARCH DIRECTIONSThis section will address some of the open issues and future research directions related to Cloud-based IoT, and which still require more research efforts. These issues include:1.StandardisationMany studies have highlighted the issues of lack of standards, which is considered critical in relation to the Cloud- based IoT paradigm. Although a number of proposed standardisations have been put forth by the scientific society for the deployment of IoT and Cloud approaches, it is obvious that architectures, standard protocols, and APIs are required to allow for interconnection between heterogeneous smart things and the generation of new services, which make up the Cloud- based IoT paradigm.2.Fog ComputingFog computing is a model which extends Cloud computing services to the edge of the network. Similar to the Cloud, Fog supply communicates application services to users. Fog can essentially be considered an extension of Cloud Computing which acts as an intermediate between the edge of the network and the Cloud; indeed, it works with latency-sensitive applications that require other nodes to satisfy their delay requirements. Although storage, computing, and networking are the main resources of both Fog and the Cloud, the Fog has certain features, such as location awareness and edge location, that provide geographical distribution, and low latency; moreover, there are a large nodes; this is in contrast with the Cloud, which is supported for real-time interaction and mobility.3.Cloud CapabilitiesAs in any networked environment, security is considered to be one of the main issues of the Cloud-based IoT paradigm. There are more chances of attacks on boththe IoT and the Cloud side. In the IoT context, data integrity, confidentiality and authenticity can be guaranteed by encryption. However, insider attacks cannot be resolved and it is also hard to use the IoT on devices with limited capabilities.4.SLA enforcementCloud-based IoT users need created data to be conveyed and processed based on application-dependent limitations, which can be tough in some cases. Ensuring a specific Quality of Service (QoS) level regarding Cloud resources by depending on a single provider raises many issues. Thus, multiple Cloud providers may be required to avoid SLA violations. However, dynamically choosing the most appropriate mixture of Cloud providers still represents an open issue due to time, costs, and heterogeneity of QoS management support.5.Big dataIn the previous section, we discussed Big Data as a critical challenge that is tightly coupled with the Cloud-based IoT paradigm. Although a number of contributions have been proposed, Big Data is still considered a critical open issue, and one in need of more research. The Cloud-based IoT approach involves the management and processing of huge amounts of data stemming from various locations and from heterogeneous sources; indeed, in the Cloud-based IoT, many applications need complicated tasks to be performed in real-time.6.Energy efficiencyRecent Cloud-based IoT applications include frequent data that is transmitted from IoT objects to the Cloud, which quickly consumes the node energy. Thus, generating efficient energy when it comes to data processing and transmission remains a significant open issue. Several directions have been suggested to overcome this issue, such as compression technologies, efficient data transmission; and data caching techniques for reusing collected data with time-insensitive application.7.Security and privacyAlthough security and privacy are both critical research issues which have received a great deal of attention, they are still open issues which require more efforts. Indeed, adapting to different threats from hackers is still an issue. Moreover, anotherproblem is providing the appropriate authorisation rules and policies while ensuring that only authorised users have access to sensitive data; this is crucial for preserving users’ privacy, specifically when data integrity must be guaranteed.VIII.CONCLUSIONThe IoT is becoming an increasingly ubiquitous computing service which requires huge volumes of data storage and processing capabilities. The IoT has limited capabilities in terms of processing power and storage, while there also exist consequential issues such as security, privacy, performance, and reliability; As such, the integration of the Cloud into the IoT is very beneficial in terms of overcoming these challenges. In this paper, we presented the need for the creation of the Cloud-based IoT approach. Discussion also focused on the Cloud-based IoT architecture, different applications scenarios, challenges facing the successful integration, and open research directions. In future work, a number of case studies will be carried out to test the effectiveness of the Cloud-based IoT approach in healthcare applications.中文译文：云计算与物联网的集成：挑战与开放问题摘要物联网（IoT）正在成为下一次与互联网相关的革命。

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