基于Web日志分析的Web Qos研究

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基于QoS本体的Web服务描述和选择机制

匹配度相结合，利用归一化算法对不同类型的ＱＳ参数取值进行标准化，ｏ最后提出了一个公平的，态的选择机制和动
相关实验结论。
关键词
服务质量，ｂ服务，Ｗｅ本体，选择机制
ＷｅｅｖｃｅｃｉｔｎａｄＳｌｔｎＭｅｈｎｓＢｓｄｏＳＯｎｏｏｙｂＳｒｉＤｓｒｐｉｎｅｃｉｃａｉｅｏｅｏｍａｅｎＱｏｔｌｇ
ｖｃ）ｉｅ。
选择服务的方法，该方法在进行语义匹配的基础上再进行数
值匹配，Ｗｅ在ｂ服务的查全率和查准率上都有所改进。
２ＱＳ本体ｏ
本体（ｔｌｇ）Ｏｎｏｙ在哲学领域称为“ ｏ存在论 ” 在计算机科，
学和人工智能领域则译为“ 本休” 。施图德等在格鲁伯和波尔斯特的定义的基础上，ｏｔｌｇ对ｎｏｙ给出了一个最完整的定ｏ
ｗｈｃｏｉｅｅｎｉｍａｃｎａｕｔｈｔｇｔｅ．ｉｇａｐｉｍｏｍａｉａｉｎａｇｒｔｍ，ｒｓｎｅｉｈｃｍｂｎｓｓｍａｔｔｈａｄｖｌｅｍａｃｏｅｈｒＵｓｎｎｏｔｃｍｕｎｒｌｔｌｏｉｈｗｅｐｅｅｔｄａｚｏ
ＡｂｔａｔＷｉｈａｔｅｅｏｍｅｔｏｅｅｖｃ，ｏｔｅｅｔｔｅｍｏｔｓｉａｌｅｅｖｃｔｉａｇｍｏｎｓＩｃ・ｔｔｅｆｓｖｌｐｎｆＷｂｓｒｉｅｈｗｏｓｌｃｈｓｕｔｂｅＷｂｓｒｉｅｗｉｎａｌｒｅａｕｔｈｄｈ

一种基于QoS的Web服务发现模型

框架内支持ＱＳ约束的语义发现，ｏ满足了用户的个性化需求。关键词：服务发现ＳＤ — ｏｏＷＳＭＱＳＱＳ约束服务质量本体服务选择算法
１引言
目前，ｂ服务应用越来越广泛，Ｗｅ受到广泛关注。
ａｇｎＵＤ语义ＷｅｕｍｅｔＤＩｂ服务注册系统基础之上，引入
ＱＳｏ模块，达到基于ＱＳ束的服务发现目的。ｏ约ＡｇｅｔＤＩｕｍｎＵＤ语义Ｗｅｂ服务注册系统的核心部
Ｗｅ服务是一种自我包含的、ｂ自我描述的、模块化的应用。Ｗｅｂ服务可以提供的功能包括简单请求到复杂商
业过程，旦一个Ｗｅ一ｂ服务被部署了，其他的应用就
述信息可以发布到ＵＤ中。它在ＵＤ的基础上增加ＤＩＤＩ
了一个语义匹配模块，并增加一个语义匹配接口来处
理Ｏ — ＷＬＳ描述的服务信息。在这里，我们对其进行扩展，将服务质量模块引入，以达到用户的个性化要求。扩展后的框架如下图１：
术主要从服务功能的角度对其进行描述，缺乏对服务质量的刻画和描述，满足不了实际的商务应用要求。当前电子商务的实际应用中，需要提供非功能属性还
请求者难以得到最佳服务。针对此问题，本文对Ｏ — 规范扩展，出一个基于ＱＳ的语义ＷｅＷＬＳ提ｏｂ服务发现模型ＳＤ — ｏ。该模型支持ＱＳ的服务发布、ＷＳＭＱＳｏ查找和反馈，并在此基础上提出一种ＱＳ约束的ｏ

《基于区块链智能合约的QoS感知的Web服务组合方法研究》范文

《基于区块链智能合约的QoS感知的Web服务组合方法研究》篇一一、引言随着互联网的飞速发展，Web服务已成为构建复杂系统与应用的基石。

服务组合技术能够将多个Web服务根据特定需求进行集成，以实现更高级的功能和服务。

然而，在Web服务组合过程中，服务质量（QoS）的保障成为一个重要问题。

区块链技术及智能合约的引入为解决此问题提供了新的思路。

本文旨在研究基于区块链智能合约的QoS感知的Web服务组合方法，以提高服务组合的可靠性和效率。

二、背景及技术基础1. Web服务组合Web服务组合是一种将多个Web服务根据业务需求进行集成，以实现更复杂功能的技术。

它通过定义服务之间的交互和流程，将单个服务组合成更大的系统。

2. 区块链与智能合约区块链是一种分布式数据库技术，具有去中心化、数据不可篡改等特点。

智能合约是一种自动执行的合同，部署在区块链上，可以实现对交易、数据等的自动管理和执行。

三、QoS感知的Web服务组合挑战在Web服务组合过程中，QoS是一个重要的考量因素，包括服务的响应时间、可用性、可靠性等。

然而，传统的服务组合方法往往忽视了QoS的保障，导致服务组合的效果不理想。

因此，如何在服务组合过程中有效地保障QoS成为一个亟待解决的问题。

四、基于区块链智能合约的QoS感知的Web服务组合方法为了解决上述问题，本文提出了一种基于区块链智能合约的QoS感知的Web服务组合方法。

该方法通过将智能合约部署在区块链上，实现对服务交互、流程、QoS等的自动管理和执行。

具体步骤如下：1. 定义服务组合的需求和目标，包括QoS要求。

2. 设计并实现智能合约，包括服务交互、流程、奖惩机制等。

3. 将智能合约部署在区块链上，实现服务的自动化管理和执行。

4. 根据QoS要求对服务进行评估和监控，确保服务的可靠性和效率。

五、方法实施及优势1. 实施步骤（1）分析业务需求，确定服务组合的目标和QoS要求。

（2）设计智能合约，包括服务交互、流程、奖惩机制等。

基于服务QoS执行信息的Web服务推荐研究

ｃａｇｓｉｔｉｐｐｒａｓｒｉｅｅｏｍｅｄｔｎａｐｏｃａｅｎｐｓＱＳｉｌｍｎａｏｎｏｔｎｉｐｏｏｅ．ｈｒｍ－ｈｎｅ，ｈｓａｅ，ｅｃｓｒｃｍｎａｏｐｒａｈｂｓｄｏａｔｏｍｐｅｔｔｎｉｆｒｉｓｒｐｓｄｅｆｅｎｖｉｅｉｍａｏＴａ
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Ａｂｔａｔｅｖｃｓｄｓｏｅｙｂｓｄｏｈｏ —ｕｃｉａ（ｏ）ｓｒｉｅｔｅａｅｅｖｄｉｃｅｓｇａｅｔｎｂｅｖｃ— ｒｓｃ：ＳｒｉｉｖｒａｅｎｔｅｎｎｆｎｔｎｌＱＳｅｖｅｆａｒｓｈｓｒｃｉｅｎｒａｉｔｎｉｙｓｒｉｅｏｉｒｅｃｏｃｕｎｔｏ — ｅｔｏｕｉｇｒｓａｃｏｍｕｉ．ｒｅｏｏｅｃｍｅＱＳｃａａｔｉｉｈｃｓｄｆｃｌｔａｑｉｃｕａｌａｄｄｎｍｉｎｅｃｍｐｔｅｅｒｈｃｍｎｔＩｏｄｒｔｖｒｏｏｈｒｃｒｔｓｗｉｉｉｉｔｏｃｕｒａｃｒｔｙｎｙａｃｄｎｙｎｅｓｃｈｆｕｅｅ

基于Qos的语义Web服务发现研究

２１００年５月第ｌ６卷第２期
安庆师范学院学报（自然科学版）
ＪｕｎｌｆｎｉｅｃｅｓｃＩｇ（ａｕａＳｉｃｄｔｎｏｒａｑｇＴａｈｒｏＩｅＮｔｒＩｃｎｅＥｉ）ｏＡｎｅｅｉｏ
Ｍａｙ２１．００Ｖ．６Ｎ２ｏＩ１ｏ．
度。本文在ｗｅ务的语义化描述的基础上，合Ｑｏ标准，出一种新的Ｗｅｂ服结ｓ提ｂ服务发现模型，过实例说明该模型能通
为用户提供最佳服务，在一定程度上满足用户的个性化需求。且
服务请求者（ｅｖｃｅｕｓｅ）和服务代理Ｓｒｉｒｑｅｔｒｅ（ｅｖｃｒｋｒ［。服务提供者向服务代理发Ｓｒｉｂｏｅ）１ｅ３布其能够提供的服务，当服务请求者发出服务请求时，务代理负责寻找对应的服务并提供给服务请求。服
关键词：Ｗｅｂ服务；义；ｏ；语Ｑｓ个性化需求
中国分类号：ＴＰ９３３
文献标识码：Ａ
文章编号：１０ — ４６（０００ —０５ — ００７２０２１）２０６３
１相关知识
Ｗｅｂ服务。目前对Ｗｅｂ服务比较统一的定
第２期
方芳，春明：于Ｑｏ的语义Ｗｅ叶基ｓｂ服务发现研究
・５・７
质量的好坏口。Ｗｅ］ｂ服务的Ｑｏ主要包括：效性（ａｌｂｌｙ、读性（ｃｅｓｉｔ）完整性（ｎｓ有Ａｖｉｉｔ）可ａｉＡｃｓｉｌｙ、ｂｉＩ— ｔｇｉ）性能（ｅｆｒｎｅ、ｅｒｙ、ｔＰｒｍａｃ）可靠性（ｌｂｌｙ、ｏＲｅｉｉｔ）可调性（ｅｕａｏｙ和安全性（ｅｕｉ）ａｉＲｇｌｔｒ）Ｓｃｒｙ。ｔ

基于动态WEB服务的QOS组合

Q O S BASED DYNAMIC W EB S ERVICE COMPOSITIONP.RAMPRABHU,M.phil.,DCA.,Librarian in Ganadipathy Tulsi’s Jain Engineering CollegeAbstractWeb Services technology allows interoperability between applications and provides flexibility to integrate information.In order to support information to information or Institution application integration, Web Services are required to collaborate. Web Service composition focuses on information process creation based on orchestration and choreography of Web Services. The manual composition of Web Service is time consuming and requires a significant amount of detailed coding dealing with various services. As the number of services increase in a composite service the orchestration among the services becomes more problematic. The necessity and complexity of the task of service composition has resulted a number of companies and standardization bodies working toward a common language for defining automation of information process executions. Some have taken the semantic Web approaches such as DAML-S and some have taken the syntactic approach such as BPML, XLANG, WSFL and BPEL (Web Service composition and these languages However, most of Web Service composition definition languages are heavily based on Web Service definitions that are provided in Web Service Definition Language (WSDL) descriptions.WSDL describes the location of the service and the operation it provides. These languages expect that the Web Services that take part in the composition to be stationary at the time of the design of the process model. These definition languages do not allow clients to select a Web Service dynamically based on the specific requirement that they have. In other words, clients cannot specify their functional and QoS requirements for specify and select the Web Services dynamically based on a set of functional and QoS criteria.In order to be able to select services at execution time, services are required to be searched and found based on specific criteria. The Universal Description Discovery Integration(UDDI) is a complementary technology to search web services located on different sites on the Internet. Existing UDDI technology uses central server (operators) to store pointers to registered web services. However, UDDI will come into the drawbacks associated with using a central system such as availability, scalability and performance. The other drawback that relates specifically to the UDDI functionality is that UDDI is not aware if the server providing the services of a registered Web Service is available and online when a client is trying to discover a service.This Paper approaches this issue by using a P2P base discovery and search of the Web Services rather than using a central scheduler. Instead Web Services would advertise their existence when they join a P2P network and are therefore available to provide services.The other motivation for this paper is to overcome the security issues that are imposed by using Web Services in a information process execution. Security is an important aspect of information collaborations. Since Web Services message exchanges occur over the Internet using HTTP based protocols, similar security threats can potentially exist. Using a P2P environment creates a virtual overlay network and creates self-organized communities. Several security technologies therefore can be enforced on these communities.Keywords:Dynamic web service, Universal Description DiscoveryIntroductionWeb Services technology allows interoperability between applications and provides flexibility to integrate information.In order to support Information-to-Information or enterprise application integration, Web Services are required to collaborate. Web Service composition focuses on information process creation based on orchestration and choreography of Web Services. The manual composition of Web Service is time consuming and requires a significant amount of detailed coding dealing with heterogeneous services. As the number of services increase in a composite service the orchestration among the services becomes more problematic. The necessity and complexity of the task of service composition has resulted a number of companies and standardization bodies working toward a common language for defining automation of information process executions.Some have taken the semantic Web approaches such as DAML-S and some have taken the syntactic approach such as BPML, XLANG, WSFL and BPEL (Web Service composition and these languages However, most of Web Service composition definition languages are heavily based on Web Service definitions that are provided in Web Service Definition Language (WSDL) descriptions.WSDL describes the location of the service and the operation it provides. These languages expect that the Web Services that take part in the composition to be stationary at the time of the design of the process model. These definition languages do not allow clients to select a Web Service dynamically based on the specific requirement that they have.In other words, clients cannot specify their functional and QoS requirements for dynamic selection of Web Services at run time. One motivation for this paper is to provide users with the opportunity to specify and select the Web Services dynamically based on a set of functional and QoS criteria.In order to be able to select services at execution time, services are required to be searched and found based on specific criteria. The Universal Description Discovery Integration(UDDI) is a complementary technology to search web services located on different sites on the Internet. Existing UDDI technology uses central server (operators) to store pointers to registered web services. However, UDDI will inherit the drawbacks associated with using a central system such as availability, scalability and performance. The other drawback that relates specifically to the UDDI functionality is that UDDI is not aware if the server providing the services of a registered Web Service is available and online when a client is trying to discover a service.This paper approaches this issue by using a P2P base discovery and search of the Web Services rather than using a central scheduler. Instead Web Services would advertise their existence when they join a P2P network and are therefore available to provide services.The other motivation for this paper is to overcome the security issues that are imposed by using Web Services in a information process execution. Security is an important aspect of information collaborations. Since Web Services message exchanges occur over the Internet using HTTP based protocols, similar security threats can potentially exist. Using a P2P environment creates avirtual overlay network and creates self-organized communities. Several security technologies therefore can be enforced on these communities.BackgroundThis chapter provides a review of the technologies used in this paper and includes descriptions of distributed computing, P2P and JXTA technologies. An overview of Web Services and Web Service composition definitions is also providedDistributed Computing Systems A distributed computing system is a collection of computing nodes that can have different types of hardware architectures and are interconnected by a communication network.There are various approaches to distributed computing systems, two of the most noteworthy ones are:•Client/Server Architecture •Peer to Peer Architecture Client/Server ArchitectureIn a network where a group of nodes is communicating with each other, a more powerful node is assigned as the server and provides services and information to the other nodes considered as clients. Clients send requests to the server and receive results. Figure 1 illustrates a Client/Server architecture view [1].This architecture has evolved from a 2-tiered architecture to a 3-tier and multi-tier client/servers architecture, where the software is modularized into two or more pieces and usually each module reside on separate hardware. Client/Server architecture provides more scalability and flexibility in softwaresystems.Figure 1: Client/Server Architecture Problem StatementThis paper focuses on the following problems:QoS in Web Service Composition Clients of Web Service compositions are bound to use the static Web Services that are defined at the time of design in a chosen Web Service Composition Language. They cannot select a specific Web Service dynamically at run time based on their requirement specifications.Web Service CompositionSecurityWeb Service composition enables information to interact and interoperate. Any transaction or message exchanged between information could potentially contain confidential information. Service providers are required to take the privacy and integrity of messages seriously.Web Service SearchandDiscoveryUDDI is used as a standard technology for dynamic search and discovery of Web Services. However, UDDI uses a central system and is not aware whether a registered Web Serviceis available at the time of client search Paper ContributionsThe developed an end-to-end solution for executing a Web Service composition. The solution is a framework that integrates best of the breed technologies with newly designed components to achieve the goals of this paper and to address the problems discussed above in a way that allows service providers to have maximum flexibility with minimum effort.•Integration of existing and new components to provide an end-to-end solution for QoS enabled webservice composition within aframework. The framework is part ofa P2P environment where an overlayvirtual network is created forenforcing security and second, adynamic search and discovery forWeb Services can be used. Also thisframework selects and binds servicesbased on defined functional and QoScriteria of the client at run time.•Developing an XML based schema for clients to define theirfunctional and QoS criteria and forWeb Service providers to presenttheir functional and QoS criteria.•Designing an enhanced search engine as a service entity inJXTA network to search servicesadvertised and dynamically selectand compose the services based onthe client’s defined criteria. Thesearch engine would be designedto support functionality that thecomposite search XML documentrequires.•Developing a prototype incorporating the above designs andconcepts. Here is a briefintroduction to the existingtechnologies used and themotivation behind them:Web Service CompositionLanguage: This paper chooses Information Process Executable Language (BPEL) as the basis ofthe Web Service composition definition language. BPEL is a dominant Web Service composition language that was originally released by IBM and backed by various vendors, and has been proposed to OASIS as a technical standard in 2003.BPWS4J: The IBM Business Process Execution Language for Web Services Java Run Time (BPWS4J) is a platform that can execute information processes written using BPEL.JXTA: To achieve a P2P environment, JXTA technology is used. JXTA, developed by Sun Microsystems, is an open, generalized P2P platform that supports core functions of a P2P system. JXTA 2, the latest release of JXTA, offers a P2P overlay network that is very scalable. Also JXTA provides a dynamic discovery service where Web Services can be found when available, in the JXTA network, thus overcoming the problems with using UDDI.P2P ArchitectureA network architecture may be called a Peer-to-Peer (P-to-P or P2P) network, if the participants share a part of their own hardware resources e.g. processing power, storage capacity, network link capacity and printer. These shared resources are necessary to provideThe service and content offered by the network (e.g.file sharing or shared workspaces for collaboration). The participants of such a network are thus resource providers (Service and content) as well as resource requestors [2].P2P is a newer architecture for distributed computing which overcomes some of the problems associated with the client/server architecture. Figure 2illustrates P2P architecturein section [1]. The following subsections provide a brief description of various P2P forms:Hybrid, pure, virtual.Figure 2: P2P Architecture NapsterNapster shares primarily music files and is an example of hybrid P2P architecture with a centralized file location directory. Users would run desktop software that enables them to share files. This software actually provides a virtual P2P network. Napster is an example of hybrid P2P architecture. There is a central database, which keeps an index of the files available in the network; when a requester queries for a specific file; the directory server provides the IP address of the user that has that file. The requester then downloads the file from that IP address directly. Although Napster once was a very popular P2P application, it was forced to shut down due to copyright violations. In Napster architecture users first connect to a central directory server to find the location of a specific file, then they will directly connect to that location and download the file from that user.Review and AnalysisThis chapter provides a review and analysis of the state of the art in dynamic Web Service composition based on QoS. An overview and analysis of QoS based dynamic Web Service compositionis presented. It provides an analysis of several approaches towards solving this issue. This chapter then specifically selects 3 related works that aligns more with the goals of this paper, and provides a detailed overview of them. A conclusion is drawn at the end that leads the paper to the chosen architecture that is provided in the next chapter.QoS based Dynamic servicecompositionWeb Service composition is the technology that enables information to provide information logic by integrating various Web Services within their organization or by collaborating with other organizations. Travel planning is an example of a Web Service composition. It involved the integration of several Web Services such as a booking a flight, booking a hotel, car rental and even booking different activities within the city. Designing the process is the first element of creating a composite service. Web Service composition language definitions are aiming at facilitating the design process.The other advantage of developing such languages is that they make the automation of the process execution possible described some of these languages: XLANG, BPML and BPEL. BPEL was specifically as it has gained wider attention than the others. They describe the modeling of a information process in a syntactic way. Introduced BPWS4J engine which executes a BPEL based information process. In fact Oracle has included a composite engine based on BPEL as part of its service architecture platform and several companies are building such composite engines.Even with the standardization of one of such languages, the design of a information process is a challengingand time consuming task. The composition of Web Services relies heavily on the WSDL description of the participating Web Services. The process designers have to decide which Web Service provider’s services to choose at design time. If a information decides to switch betweens providers, the design process has to change accordingly.On the other hand, service providers publish different WSDL documents. Even for similar functionality,but they might define different operation names or signatures. That is one reason the changing of a process definition language requires more effort. An agreement on Web Service providers with similar services to provide their WSDL document based on a template, greatly saves time and effort for information. Even if the same functionality and WSDL is agreed upon for similar services,providers follow different QoS. QoS specifies qualitative and quantitative aspects of a Web Service. Web Service composition designers and users will benefit significantly in their selection of Web Services if they know the QoS associated with a Web Service. One approach for providing QoS information of services has resulted in definitions of XML specifications that provide the details of QoS. Web Service Offerings Language (WSOL) is one of such languages. WSOL enables formal specification of classes of service in a Web Service. Classes of service are referred to as service offerings of a Web Service. WSOL is a thorough and strongly typed language definition that allows specification of functionalWeb Service ArchitectureIn this chapter the detailed architecture of the Web Service Composition QoS Enabler Framework(WSQEF) is presented. The following describes the requirements of the design. An overview of the third party components used in this platform is provided. Then a diagram is provided to illustrate all the components in the framework and their interactions followed by subsections that provide detailed descriptions of the components. The last subsection provides interaction diagrams describing the sequence of execution.Requirement examinationWSQEF is a platform with the design goal of enabling the late binding of a series of services that can execute a composite service based on the specified criteria for each of the Web Service. The following lists the requirements that WSQEF will try to fulfill:•Providing the service provider and client with a secure environment.•Ability to provide QoS in a generic way for more flexibility.•Ability to add service providers in a scalable way.•An end-to-end solution to compose two or more web services.•Enabling clients to select the services that best match their functional and QoS requirements.•Enabling service providers to provide their QoS information in the network.•Ability to find required web services with reasonable performance.Integrating the existing technologies with the new components to exploit latest technologies and standards.Illustrates the system requirement in an abstract way. At a high level, this system requires creating a DynamicWeb Service composition based onthe client’s functional and Qoscriteria.ImplementationIn this chapter implementation details of the WSQEF framework are provided. A diagram is presented that shows an overview of the implementation details followed by subsections describing the components in the diagram. The main class diagrams and sequence diagrams of the implementationare presented in this section as well.Figure3presents an implementation view of the WSQEF with regards to JXTA architectureThe main components of WSQEF are:•WSCS(WebService Composite Search)and WSQS(Web ServiceQoS) XML documents. Thesetwo documents are based onComposite Web ServiceSearch Criteria schema, thecomplete design of theschema .•The Web Service QoS Advertisers, they will be executed as peers in the JXTA network and advertise the WSQS document along with the WSDL location of the service they are advertising. They use “parm” element of the modulespecification advertisement that is described to advertise their WSQS information. An example of WSQS. The design details of this component are provided.•The Enhanced Search Service, the search service will be executed as a peer in the JXTA network, the design details of this component are provided.• The Web Service Composition Client, the client will also join the JXTA network as a peer, the design details of this component are provided.These components are part of the JXTA application layer, and use the services provided in the JXTA community layer (discovery and advertisement) and JXTA Core layer (peer, peer groups and pipes). JXTA Architecture layers are described.In this chapter implementation details of the WSQEF framework are provided. A diagram is presented that shows an overview of the implementation details followed by subsections describing the components in the diagram. The main class diagrams and sequence diagrams of the implementation are presented in this section as well. REVIEWThis chapter presents an experiment of the concepts and designs that were described in previous sections. It is a prototype implementation of the WSQEF framework. JAVA, XML and JXTA and BPEL technologies are used throughout the implementation. In this section we walk through an example of a Web Service search composition in JXTA in WSQEF framework. The chapter describes an example scenario in and provides the necessary steps that take place for the scenario to execute a Web Service composition based on WSQEF architecture.Example circumstancesIn this example, the process ofa loan request is implemented. The process begins with a customer requesting for a loan. The information process then sends the request to a financial institution and receives the result. Then it will send the request to an assessment company and asks for the risk associated with the loan. If we were going to implement this information process without using WSQEF Framework, we would have to choose a fixed financial institution and a fixed assessment company in the information process that define a Web Service composition scenario. In contrast, in the WSQEF framework, a number of financial institutions and assessment companies can take part in the information process provided that they agree on a set of interfaces and the same service names. The framework decides at run time, which one of the service providers qualifies for taking part in information process execution. Port Types determine what service to bind at execution time. The framework assigns them values that are selected Web Services based on the customer’s criteria. The following subsections describe the steps that fulfill this information process executionRaw BPEL DocumentA set of WSDL documents and one BPEL document are required to be written for this information process. At design time we assume that the WSDL documents of the financial institutions that provide loan services through a Web Service are available and they all support the same operations and messages, see Table 24 for the WSDL document of this Web Service example. The same applies to assessment companies; they all have the same interface for providing assessments on loans, see Table 25 for the WSDL document of this Web Service example. A WSDL document that defines the process interfaces is called the raw BPEL document. In this document the port Types define the service bindings at run time. In WSQEF the port Types are placeholders that will be filled during the information process execution. A BPEL is required to define the process of this Web Service composition scenario, see Table 26 for this BPEL document. In the Raw BPEL WSDL document (Table 16) these elements are highlighted, the value of port Type elements are the Web Service names at this point. A Web Service name is a name contracted between the consumers and service providers, each set of Web Services that provide similar services will agree on the same name.Reference1.Data Communication and Netrowrking by Behrouz A Frouzan-TMH.2.Data Compputer communication by William stallings-Pearson Education.3. Computr Networks By L.Peterson and Bruce S.DaveDictionaryDAML-S Draper Agent Markup Language for serviceBPEL Business Process Execution. BPML Business Process Modeling. QOS Quality of ServiceOASIS Organization for the Advancement of structured information standards.UDDI Universal Description, Discovery and Integration.WSDL Web service Definition LanguageWSFL Web Service Flow LanguageXML Xtensible Mark-up Language JXTA Juxtapose.Xlang is an XML Based.。

一种基于日志的Web应用性能测试流量描述方法[发明专利]

专利名称：一种基于日志的Web应用性能测试流量描述方法专利类型：发明专利
发明人：宋伟,张玉军,贺柳
申请号：CN201710380273.7
申请日：20170525
公开号：CN107193744A
公开日：
20170922
专利内容由知识产权出版社提供
摘要：本发明提供了一种基于日志的Web应用性能测试流量描述方法，包括四个步骤：日志数据预处理步骤、用户行为模型构建步骤、用户类型分布信息提取步骤和时间分布特征提取步骤。

本发明通过从后台日志中提取包括用户行为特征、用户类型分布特征和强度时间分布特征来共同描述Web应用流量，实现了对Web应用性能测试流量的真实的描述，满足了性能测试对测试流量真实性的需求。

申请人：中央民族大学
地址：100081 北京市海淀区中关村南大街27号
国籍：CN
代理机构：北京东方盛凡知识产权代理事务所(普通合伙)
代理人：宋平
更多信息请下载全文后查看。

基于日志挖掘的web服务带宽资源管理策略

上海工程技术大学学报JOURNAL OF SHANGHAI UNIVERSITY OF ENGINEERING SCIENCEVol. 33 No. 4Dec. 2019第33卷第4期2019年12月文章编号：1009 - 444X(2019)04- 0384- 08基于日志挖掘的Web 服务带宽资源管理策略樊莹〔，胡建鹏12,孙天齐〔，黄娟1收稿日期：2019 - 06 - 13基金项目：国家自然科学基金资助项目（61802252）作者简介：樊莹（1992-）,女，在读硕士，研究方向为数据挖掘、云计算.E-mail ： fy_sues@通信作者：胡建鹏"980 -），男，副教授，在读博士，研究方向为软件工程、数据挖掘、服务计算.E-mail ： mr @S ue S . edu. cn(1.上海工程技术大学电子电气工程学院,上海201620；2.上海交通大学电子信息与电气工程学院,上海200240)摘要：Web 服务通常部署于云上，研究用户负载强度的变化对其性能的影响至关重要，且在保证服务质量的同时还需要考虑如何提高资源利用率和控制成本.提出一种基于日志挖掘预测Web服务带宽消耗和服务质量的方法，为带宽资源管理提供决策支持.基于回归分析和函数拟合，该方法亦可估测服务器在限定带宽条件下的服务能力，以及不同带宽配置对服务质量的影响.利用公开数据集和基准测试试验对该方法的有效性和准确性进行评估.关键词：Web 服务；日志挖掘；带宽消耗；响应时间；回归分析；资源管理中图分类号：TN 248.1 文献标志码：ABandwidth Resource Management Strategy ofWeb Service Based on Log MiningFAN Y%g' , HU Jtanpeng 12 , SU* Ttanqt 1 , HUANG Juan 1(1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China ；2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong Universty, Shanghai 200240, China)Abstract : Web services are usually deployed on the cloud. It is important to study the impact of changesin user load strength on their performance. In addition to ensuring service quality, it is also necessary to consider how to improve resource utilization and control costs. A method based on log mining to predictbandwidth consumption and service quality was proposed , which can provide decision support for bandwidthresourcemanagement Basedonregressionanalysisandfunctionfi t ing ,the methodcanalsoestimate the server's service capabilities under defined bandwidth conditions and the impact of differentbandwidth configurations on service quality . The validity and accuracy of the method were evaluated by usingpublicdatasetsandbenchmarktestsKey words ： Web service ； log mining & bandwidth consumption & response time ； regression analysis &resourcemanagement目前，许多Web 服务系统规模庞大且越来越复杂化，但在云计算时代这些问题都能够很好地解决，因为云可以提供灵活的资源供给和定价方式,所以Web 系统经常被托管在云上.云应用的资源第4期樊莹，等:基于日志挖掘的Web服务带宽资源管理策略・385・需求会随着负载强度的变化而变化，因此进行资源管理并提前预测资源的变化是十分必要的.现有的研究方法大多只关注一两个主要的计算资源（如CPU'1-2（、内存囚、硬盘I/O"等），其中研究CPU 资源的最为普遍，但对Web系统来说，带宽资源经常成为瓶颈，而且带宽在云市场中也是相对昂贵的资源，大多数共有云服务商对于带宽资源的计费通常采用阶梯定价策略，带宽消耗越大单价就越高，所以了解Web系统的带宽是如何被消耗的对于资源管理来说非常重要，特别是那些分布在不同的虚拟机上的大型Web系统.日志挖掘在结构优化方面的应用最为广泛，但在资源管理决策支持方面研究较少，尤其是在带宽资源方面.本文主要针对Web系统的性能及资源管理方面进行研究，且主要关注带宽资源.本文通过回归分析对带宽消耗进行预测，通过函数拟合在限定带宽条件下估测可供应的用户数，以及给定用户数时所需要的平均响应时间，为带宽资源管理提供决策支持•研究目标有两个：1）在负载强度变化时准确预测带宽消耗；2）估测服务器在限定带宽条件下的服务能力，以及不同带宽配置对服务质量（Quality of Service,QoS）的影响•1相关工作!1日志挖掘相关研究基于日志挖掘的相关研究已经有很多，在系统结构优化方面，于华等「6（提出一种基于Web日志挖掘的网站优化方案，但该方法只考虑了用户访问序列模式，基本上没有考虑在用户访问序列基础上Web页面的内容语义，而且在应对大数据量的日志时,处理过程的效率不高.在故障预测和行为检测方面，钟将等「7（研究表明，网络中大部分的故障可以通过网络运行日志数据进行预测，该方法优于基于威布尔分布的预测模型，但在特征提取规则%算法的提升等方面还可以改进.在资源优化方面的研究,孙亚楠等⑻在点对点（PMP）模式下设计一个有效率的QoS调度算法，该算法能够在保证QoS需求的同时，有效地分配带宽资源.刘林东等⑼提出一种基于日志的资源发现和资源分配算法,该算法能有效地根据日志信息进行资源发现和分配，有效提高了资源发现和分配的效率，为资源再分配和动态分配提供决策支持•但其主要是针对CPU资源进行研究，没有研究带宽资源•综上，现有研究对带宽分配研究有较多而对带宽进行预测研究得很少，基于日志挖掘对带宽资源进行研究的更少•1.2性能和资源管理方面研究现有关于性能和资源管理方面的研究考虑带宽资源的很少：Verdicket等「10（尝试在假设分析中将一个网络模拟器集成到一个排对网络仿真（Queuing Network Simulation,QNs）的分析性能求解器中，虽然这些技术可以提高网络密集型系统预测的准确性，同时兼顾CPU和网络资源，但一个关键问题是其可能需要在解决QNs和网络仿真之间进行多次迭代才能达到足够接近的网络延迟估计,并且不能保证这种迭代的收敛性•Zhang'111提出一种个性化的QoS预测方法，该方法考虑网络、服务器环境和用户输入的影响，使用机器学习和协同过滤来预测每个单个请求的QoS,但它不能预测带宽消耗，也不能对新环境中发生的问题进行预测•综上所述，基于日志挖掘的广泛应用和成熟性以及带宽资源管理的必要性，本文提出基于日志挖掘对带宽资源进行管理的策略研究，通过理论分析和实验评估，对带宽消耗进行预测，并对不同负载强度下的服务能力进行估测.2日志挖掘方法本文方法主要由两个步骤组成:第1步为日志挖掘；第2步为分析与预测•由于用户行为的高度复杂性及日志数据的庞大性，大部分的时间和精力都花费在日志挖掘上.日志挖掘方法的结构框图如图1所示.图1日志挖掘方法结构框图Fig.1Block diagram of log mining method2.1数据预处理日志挖掘需要使用Web日志中的以下数据字段:用户的IP地址、请求日期和时间、请求项的统+386+上海工程技术大学学报第33卷—资源标识符(Uniform Resource Identifier,URI)%协议状态和发送的字节数.在原始日志中，每一行代表一个请求项，它可能是一个Web头页、Javascript 文件、图像或嵌入式子页面.在预处理阶段，每个请求项都被识别并标记为一个对象类型的字段；对URI进行过滤并且选取协议状态等于200的请求项;请求日期和时间组合在一起并转换为时间戳格式.因类型为hqx、zip、mov的单个文件很大，会引起发送总字节产生很大的波动,因此将字节数大于1MB的请求记录过滤掉，后文将详细描述.单个Web服务请求或页面请求通常包含多个请求项,添加请求块字段block来标记隶属于同一个页面调用的所有请求项，每个请求块由它本身的ID和请求项的URI来唯一标识.2.2服务分组通过服务分组可以评估不同服务请求所产生的影响.用户通过执行服务请求与Web系统进行交互，这些服务请求在结构上可能非常相似，但并不相同.服务分组可识别具有相似访问模式的不同服务请求，因此可以将类似的请求分组到同一个服务类中，许多基准测试应用程序(如TPC-W'12]% SPEC'13等)在其负载生成器中就定义不同的行为类型.服务分组后，有些服务类对服务器负载的贡献很小，在后续实验中将忽略这些服务类.2.3时间分片本文设定一个固定时间长度的观察窗口，将其看作一个时间分片.服务分组之后将Web日志分成许多片，分组的时间长度和时间分片相同.例如，将时间分片的长度设定为10min,那么24h的日志就被划分为144个时间分片.对于系统管理员来说，衡量带宽消耗都是在一个时间段内进行统计的，通过时间分片不仅可以使复杂的访问简单化，且有利于预测结果和实际数据的对比.2.4用户聚类通过用户聚类容易获得每一类用户集群的信息及其特点，并可对不同用户集群在多方面进行比较，找出它们之间的差异性.基于行为对用户进行分类有利于提高预测不同负载强度下带宽消耗的准确度.本文使用基于质心的X-means算法'⑷对用户进行聚类，无须指定用户类别数，即可寻找最优的类别划分方法.聚类之前需要计算在一个时间窗口内代表每个用户兴趣度的向量，选择这些向量作为聚类的输入.在一个时间窗口内，用户对服务类2的兴趣度「定义为(1)--1式中:*为用户对服务类-的请求数；"为服务类别的总数2.5多元线性回归多元线性回归模型有多个输入变量，本文使用每个用户集群的用户数作为多元线性回归的输入,页面请求数和发送的总字节数分别作为输出,因具有相似行为模式的用户被划分为不同的集群，故不同集群的用户产生的总请求数和发送的总字节数分别表示为Requests-•+E±%(2)k-1BytesSend-'!•+k±%(3)k-1式中：+k为每个用户集群的用户数量；#k和0k分别为相应负载强度的系数;％为误差项&为用户集群的类别数.若不对用户进行分类，直接以总用户数作为输入进行线性回归，则上述公式退化为单元线性回归，下节实验中将对这两种方法分别进行评估2.6分段函数拟合通过试验观察和基于文献[15(中用户数和响应时间的关系研究，当用户的地理位置确定时，用户数和响应时间的关系可由一个分段函数表示，该函数前半部分符合一元线性函数，即带宽利用率在一定范围时，随着负载强度的增加，用户平均响应时间变化不大;后半部分符合二次函数，即当负载增加到一定程度时，用户平均响应时间会迅速上升.本文给出此函数的具体定义为：定义变量八使r-a-V/L,即预期带宽需求比例，作为此分段函数的自变量.式中！为常量，表示每个用户实际消耗的网络吞吐量的平均值，通过分析访问日志得出；V为用户数;L为限定带宽的大小.分段函数表示为-N,V/L$r0T-,(6).f(a-V/L),V/L>r0式中:T为平均响应时间*为分段函数前半部分稳定期中用户平均响应时间的平均值；r为阈值常量，用来区分两个不同的函数段.一般来说，网络密集型Web服务在预期带宽需求比例达到90%以上时，平均用户响应时间才会有明显上升，故在下节试验评估中将r设置为0.9.这是一个经验设定值，不同系统的阈值可能是不同的，还需要考虑第4期樊莹，等：基于日志挖掘的Web服务带宽资源管理策略-387-其他资源的竞争状况.3试验评估与结果分析本文设计两个试验：试验一是基于多元线性回归的带宽消耗预测；试验二是利用TPC-W基准测试试验获取相关日志，基于分段函数拟合来估测带宽增减时响应时间的变化.3.1预测带宽消耗3.1.1数据处理本文采用的公开数据集为1998年世界杯网站的访问日志,共有1352804107条请求记录.本文选择第42天（day42）和第43天（day43）两天的日志数据进行试验.day42有5452684条请求记录，预处理后检测到187099个请求块，服务分组的类别参照文献'6（中的方法，共有26种服务类，见表1.其中，13种服务类对总字节的贡献超过95%,为简化试验,提高计算效率，进行用户聚类时只选择这13种服务类,定义为S1至S13,用户聚类后的结果见表2.由13种服务类在某个用户集群里的带宽消耗占比可以看出6个用户集群对这些服务类的兴趣度和带宽消耗量不同，聚类后可以看出某些用户集群对某些服务类的带宽需求占比很小.为使模型更加简化，忽略平均发送字节占比少于5%的服务类，同时确保余下的用户集群总字节占比在90%以上.对day43日志数据的处理与day42相同.表1服务分组Table1Service grouping服务类总字节数/MB贡献百分比/% /teams20247%40246%/mainlevel%66%6%902020/competition%37827%0%676/news%0697330%300/playing3995305486/venues2348240285/history/past%9987%6243/tickets%829395222/playing/mascot%638005 1.99/history%63040% 1.98/venues/cities%363267%66/playing/download%285%8% 1.56/enfetes%%59689 1.41/hosts/cfo%008654%23/history/history7%7452087/venues/venues5%3%33062/member43%285052/hosts/suppliers36807%045/help297362036/hosts/fifa%089330.13/individuals7083%009/history/reading5936%007/hosts/sponsors48053006/hosts/f400%6005/playing/rules0540900% unknown000%7000求和82260%50%0000表2用户集群的兴趣度占比Table2Interestingness proportion of user cluster%用户用户数_____________________________________________________服务类集群占比/%si S2S3S4 S5S6S7S8S9S10S11S12S13 C03667%69202438502%009329562455263505627622%23% C19.124.19 1.530760220038827080029030234067036023 C2 1.100.150060020.18993600500600%00%0.1000000%000 C31.18500406 1.95475727%%2.1739% 1.34 1.09 1.87 1.97 1.56038 C42688969706%0300080020760220060.18064008007002 C52423534811.1035805902%%272263%48 1.95923%660880473.1.2基于日志挖掘结果的服务分离策略在实际系统运维中，可以根据日志挖掘结果将某些服务请求转移到其他专门配置的服务器.例如，根据服务请求属于计算密集型还是属于网络传输密集型进行分离&对于带宽消耗稳定的用户采用包年包月策略的服务器；属于偶然性下载消耗大量带宽的用户采用按量付费策略的服务器.通过分析上述试验日志发现，存在小部分大文件下载・388・上海工程技术大学学报第33卷会偶然性地消耗大量带宽的情况，为消除不确定因素的影响，这里采用服务分离策略将试验中大文件下载请求过滤掉，只针对稳定的带宽消耗进行研究.对day 42日志进行日志挖掘处理后，得到总访问用户数与总字节数之间的关系，如图2(a)所示.由图可见，两者间的线性关系并不好，且经过计算得到单元线性回归预测模型的决定系数R 为38.8%,多元线性回归预测模型调整的R 2为40. 53%™，显然拟合程度很差•采用服务分离策略后，忽略所有大于 1 MB 文件的下载请求，得到的总访问用户数与总字节数的关系如图2(b)所示.由图可见，两者有较好的线性关系，且经过计算得到单元线性回归预测中总字节数的R 2为87.1%，多元线性回归预测中总字节数调整的R 2为90. 31%，这极大提高了带宽消耗的可预测性.60 00050 00040 00030 00020 00010 000300 350 400 450 500 550 600总用户数(a)未采用服务分离策略28 00026 00024 00022 00020 00018 00016 00014 00012 00010 000300 350 400 450 500 550总用户数(b)采用服务分离策略图2总用户数与总字节数的关系图Fig. 2 Relationship between total numbers of users and total numbers of bytes3. 1. 3 单元与多元线性回归拟合程度比较对于单元线性回归，将day 42中用户数范围为280〜350的总用户数、总请求数及总字节数数据作为训练数据，分别训练出两个线性模型，即总用户数分别与总请求数和总字节数的线性模型•单元回归拟合结果为：总请求数的R 2二97. 8%，总字节数的R 2=94. 1%.对于多元线性回归，将day 42中用户数为280〜 350所对应的6类用户集群数量、总请求数及总字节数的平均值作为训练数据，分别训练出两个线性模型，即6类用户集群对应的用户数分别与总请求数和总字节数的线性模型.多元回归拟合结果为：总请求数的R 2 - 99. 36%，总字节数的R 2二99. 92%，既使用修正后的决定系数，总字节数调整的R 2也有99. 44%.采用单元与多元线性回归拟合进行总体比较可以发现，多元模型的拟合程度比单元的好，因此后续试验选择多元线性回归做预测•3. 1. 4 多元线性回归预测为不失一般性，用day 42的线性回归模型来预测day 42其他数据和day 43数据，正常情况下，对当天的预测效果比对其他天预测效果好•通过上述拟合精度的比较，得到两个训练模型，分别运用这两个模型预测day 42和day 43的总请求数及总字节数.day 42和day 43的预测数据与上面训练数据的选取类似，唯一不同的是用户数为360〜420.最后使用均方误差、平均绝对误差和相对误差等3种精度测量法评估预测的准确性，结果见表3.由表可见，对当天的预测效果更好，符合最初的设想.表3 6个用户集群的误差比较Table 3 Comparisonoferrorsbe4weensixuserclus4ers时间平均绝对误差均方误差相对误差/ %总请求数/条总发送字节数/KB总请求数/条总发送字节数/KB总请求数总发送字节数day 4222 420.897 99821.175 61. 885. 23day 4338. 132 454128!! 6!7213. 1614. 98上述试验是在有6类用户集群的条件下进行 C 2和C 3,进行相同试验操作，单元拟合结果为：总的,若忽略2个对总发送字节贡献很小的用户集群请求数的R 2 - 92. 9%，总字节数的R 2 - 89. 0% ；多第4期樊莹，等：基于日志挖掘的Web服务带宽资源管理策略・389・元拟合结果为总请求数调整的X-97.34%,总字拟合程度好.通过多元线性回归拟合预测得到的试节数调整的疋-95.83%,仍然是多元拟合比单元验结果见表4.表44个用户集群的误差比较Table4Error comparison of four user clusters平均绝对误差均方误差相对误差/%时间--------------------------------------------------------------------------------------------------------------------总请求数/条总发送字节数/KB总请求数/条总发送字节数/KB总请求数总发送字节数day4230.540.344917090.3433 2.79 2.39day43100.30 1.104712260 1.87698.33 6.92由表可见，3种误差水平总体上均有所上升,对day43中总字节数的预测精度比对day42中总字节数的预测精度高.所以单纯对字节数进行预测时只选取贡献较大的几个用户集群即可达到近似的准确度通过单元与多元线性回归拟合程度的比较，得出多元拟合的预测效果更好；采用day42部分数据对day42的其他数据和day43数据进行预测,得出对当天进行预测的精度更高，说明用户行为会随着时间变化有所变化，若误差过大可考虑重新对最近的日志数据进行训练然后再做预测模型；多元线性回归做预测时，分别采用6个用户集群与4个用户集群进行试验，所得结果各有优势，因此为简化试验模型，可以忽略贡献小的用户集群.3.2估测带宽增减时响应时间的变化3.2.1试验环境设置本文使用TPC-W基准测试系统进行实验，服务器端部署在1核2GB内存的阿里云深圳节点的虚拟机上，客户端部署在相同配置的若干异地的阿里云虚拟机上.安装并启动性能监控程序后，每次试验结束时会生成相应的性能日志.在服务端限定带宽为'〜5M时设置各自的用户数范围，用户数都设置到各自能承受的最大值;每个限定带宽条件下，进行客户端日志、性能日志以及访问日志的数据采集，设置的带宽越大,其所能承受的负载也越大;试验运行时间设置为0.5h,并且选取Home 页面的平均响应时间进行度量比较通过客户端采集的数据可获得平均响应时间$在限定带宽下每个用户数对应一个平均响应时间；通过性能日志可获得服务器的吞吐量$在限定带宽下每个用户数对应一个服务器的吞吐量;对于Web服务的访问日志文件，相同的限定带宽对应的不同用户数下，日志的请求数不同.例如，带宽为3M、用户数为60时,采集的总请求数为141655条，经预处理后，检测到784个请求块;用户数为85时，采集的总请求数为170296条，经预处理后$检测到1$94个请求块后续对于日志数据的处理类似于前文中对世界杯数据的处理,处理时时间窗口长度设置为3min.3.2.2负载强度与网络吞吐量及响应时间的关系通过进行不同负载强度试验，可实现客户端网络流量的最大化,并得到网络吞吐量和响应时间的变化趋势.采集'〜5Mbit/s的数据，发现用户数与网络吞吐量的总体变化趋势以及用户数与响应时间的总体变化趋势都是类似的，因此以3M 带宽为例进行分析用户数与网络吞吐量的关系如图3(a)所示.随着用户数的增加，网络吞吐量也在增加，当用户数达到75时，带宽消耗接近阈值，限定的吞吐量开始产生大量的传输控制协议(TCP)再重传，而实际传输给用户的吞吐量并没有明显增加.此外，实际传输给用户的吞吐量在接近阈值之前，它与给定的吞吐量之间总存在约10%的差距,这是因为性能监控记录的是网卡的发送总字节数,其中含有背景流量且含有各层网络协议数据包中的所有字节，而Web服务日志中只记录应用层发送文件数据的总字节数用户数与平均响应时间的关系如图3(b)所示.在初始阶段用户数小于60时，随着用户数的增加，响应时间在364ms上下浮动，变化不大；当用户数由70增加至85时，页面响应时间迅速增加.从图3还可知，如果将平均响应时间限制为15s,那么这个服务器可以为80个用户提供服务，因此，有时只要满足QoS的约束，服务器的轻微超载是可以接受的.3.2.3带宽增减时进行预测基于分段函数拟合，通过训练2M和3M带宽数据得到一个模型，然后利用该模型分别预测4M和5M带宽下用户数与平均响应时间之间的关系.带宽为'〜5M时用户数与平均响应时间的实际关系如图4所示.用户数与平均响应时间的关・390・上海工程技术大学学报第33卷图4不同带宽的用户数与平均响应时间的关系图Fig. 4 Relationship between user numbers with differentbandwidthsandaverageresponsetime系符合分段函数式(6),因此可分为两个阶段进行研究•研究发现，不同带宽下两个阶段的分段节点均约为当前用户数与当前带宽的比值•通过计算,不同带宽下的预期带宽需求比例厂0的阈值基本都在0. 9左右.在平均响应时间稳定阶段,带宽为2〜5 M 时,计算每个用户实际消耗的网络吞吐量•然后将2〜5 M 带宽下的平均响应时间及每个用户实际消耗的网络吞吐量分别求平均值，此时的平均响应时间的平均值为366 s,即分段函数中的N .将其作为稳定期间的响应时间，常量a 取值为0. 040 455 Mb/s.在平均响应时间快速变化阶段，带宽为2 M 和3 M 时，将相应数据代入分段函数，可得快速变化阶段中各响应时间与c 的关系，然后将2 M 和3 M 带宽的响应时间数据和厂字段数据分别汇总，通过数据分析工具可得到平均响应时间与厂的拟合模型，且该模型符合二次函数.带宽为2〜5 M时实际的平均响应时间与厂的关系如图5 (a)所示•分别计算4 M 和5 M 带宽数据的厂值，再将c 值分别代入上述拟合的二次函数模型，即可得到相对应的预测平均响应时间，最终预测值与实际值间的关系如图5(b)所示.通过计算可知，4 M 带宽的预测值与实际值的平均相对误差为14. 83%，而5 M 带宽时的平均相对误差为6. 32%.-»-2M --*-3 M 一14 M0.8 0.9 1.0 1.1 1.2(a)实际曲线-0-5 M 预测曲线-亠-5 M 实际曲线-»-4M 预测曲线4 M 实际曲线0.80 0.85 0.90 0.951.00 1.05 1.10r(b) 4 M 与5 M 带宽预测曲线图5带宽需求比例与平均响应时间的关系图Fig. 5 Relationship diagrams of bandwidth demand ratio and average responsetime第4期樊莹，等：基于日志挖掘的Web服务带宽资源管理策略-391-4结语本文提出一种基于日志挖掘快速预测带宽消耗和响应时间的方法，该方法可以为Web服务的带宽资源管理提供决策支持，具体可以应用在以下几个方面：针对不同的负载强度，在一定误差允许范围内可以预测带宽消耗；在限定带宽条件下，可以预测系统的最大承载能力；在给定负载强度下配置合适的带宽，能够在满足服务质量的同时提高网络利用率;通过已知的限定带宽下用户平均响应时间的变化规律可以估测未知限定带宽下响应时间的变化规律.本文实验结果还表明：有时只要满足QoS约束，服务器的轻微带宽超载也是可以接受的.本文针对不同的负载强度对带宽消耗和响应时间进行研究，后期将对其他资源的利用率和QoS 进行研究，并使用建模与仿真的方法进行演化场景下的性能预测参考文献：:1]何子龙，陈宁江，黄汝维，等.TenantCPUMan：基于负载分析的多租户动态CPU资源调整[J].计算机应用与软件，2016,33(12):11-14,19.'2(KOZIOLEK H，SCHLICH B，BECKER S，et al.Performance and reliability prediction for evolvingservice-oriented software systems[J(.EmpiricalSoftware Engineering,2013,18:746—790.[3(廖旺坚，黄永峰，包从开.Spark并行计算框架的内存优化[J(计算机工程与科学，2018,40(4)587-593. [4(李强，孙震宇，雷晓凤，等.基于磁盘I/O性能的Hadoop任务选择策略计算机工程，2016,42(11):76-82[5(周渭博，钟勇，李振东.基于存储熵的存储负载均衡算法计算机应用，2017,37(8)2209-2213.[6(于华，张文盛.基于Web日志的网站优化方案的设计与实现[J(办公自动化，2012(12):23-24,29. [7(钟将，时待吾，王振华•基于告警日志的网络故障预测计算机应用2016,36(S1)：49-53,13.[8(孙亚南，潘中强.IEEE802.16中支持QoS的有效带宽分配机制[J(计算机与现代化,2016(9):25-29,34. [9(刘林东，邬依林•网格资源发现和分配算法研究[(•计算机仿真，2012,29(12)115-118,250.[10(VERDICKT T,DHOEDT B，DE TURCK F,et l Hybridperformancemodelingapproachfornetworkintensive distributed software[C("Proceedings ofInternational Workshop on Software andPerformance.Buenos Aires：ACM,2007：189—200. [11(ZHANG L,ZHANG B,PAHL C,et l Personalized qualitypredictionfordynamicservice managementbased on invocation patterns[C("Proceedings ofInternational Conference on Service-OrientedComputing Heidelberg：Springer2$13：84-98 [12(刘莉，屈培.基于Web服务器性能的TPC-W基准测试[J(.计算机工程与设计，2008(11)2769-27712775[13(OTT M,STAMATAKIS A.Preparing RAxML for the SPEC MPI benchmark suite[M(〃IN WAGNERS$STEINMETZ M$BODE A$et al HighperformancecomputinginscienceandengineeringBerlin：Springer2$1$：757-768[14(PELLEG D,MOORE A W.X-means：Extending K-meanswithe f icientestimationofthenumberofclusters[C("Proceedings of International Conference onMachineLearning HeidelbergSpringer，2$$$727-734 [5(冯翠霞，张晓明•基于OPNET的分布式网站监测与性能分析的研究[(北京石油化工学院学报,014,22(2)：14-19[16(VOGELE C,VAN HOORN A,SCHULZ E,et l WESSBAS-extraction of probabilistic workloadspecifications for load testing and performanceprediction：A model-driven approach for sessionbased application system[J(.Software&SystemsModeling,2018,17:443-477.[7(冯瑶，侯亚楠.基于拟合函数对测试指标估算的研究[J(.中国金融电脑，2019(8):61-66.(编辑：韩琳%。

综合时空信息的Web服务QoS预测方法研究

第 17 卷第 18 期 (2021 年 6 月)
Web 服务项的 CF 算法对不同数据集的依赖程度，不仅提升了信息的利用价值，还提高预测的准确度。
基于历史记录的 CF 算法是使用现有的数据进行预测，是一种启发式算法。这种算法虽然计算简单，但由于 QoS 值的不完整性和不稳定性，存在 QoS 矩阵稀疏性和在处理大量数据时的时效性等问题。同时，该算法对历史数据集的依赖性比较大，存在一定的局限性，例如冷启动问题，忽略了网络环境等外部因素的影响。
2）基于模型（model-based）的协同过滤算法近年来，随着机器学习的兴起，已有学者提出多种基于模型的协同过滤方法，除了可以克服 QoS 矩阵极度稀疏和在处理大量数据时的时效性等问题，在推荐效果以及算法可行性方面均有突破。基于模型的协同过滤是通过各种机器学习算法对历史 QoS 数据进行训练得到一个学习模型，再使用此学习模型进行 QoS 预测。目前，基于模型的 CF 算法有矩阵因子模型、聚类模型、潜在语义模型、贝叶斯模型等。下面我主要介绍基于矩阵分解（MF）的各个模型（如表 1 所示）。
任意给定的一个非负矩阵可分解为左右两个非负矩阵的乘积,并用系数矩阵 H 代替原始矩阵，对原始矩阵进行降维，得到数据特征的降维矩阵，从而减少存储空间。
张量分解（TF） (Tensor Factorization)
它可以看作是矩阵奇异值分解和主成分分析的高阶泛化，常见的分解是 CP 分解和 Tucker 分解。近年来，其在图像处理等领域得到了一些广泛的应用。
2 Web 服务 QoS 主要预测方法说明
2.1 基于内容（content-based）的预测方法
基于内存的预测算法使用用户服务数据库的整个或一个样本来生成预测。这种算法易于实现且效率很高，直观易懂、更容易解决冷启动问题、容易落地到真实的业务场景中，但推荐精准度不太高，且无法扩展。基于内容的推荐算法有很多的应用实例，比如今日头条中有很大比例是基于内容的推荐算法。

时间感知的Web服务QoS预测方法研究

时间感知的Web服务QoS预测方法研究时间感知的Web服务QoS预测方法研究摘要：Web服务广泛应用于各种应用程序和企业级系统中，因为这些服务可以提供方便和灵活的方式来实现复杂的业务逻辑。

然而，提供高质量的Web服务（QoS）一直是一个挑战，因为Web服务依赖于许多因素，如带宽、响应时间和可靠性等。

为了提高Web服务的质量，QoS预测方法是必需的。

在本文中，我们提出了一种时间感知的Web服务QoS预测方法。

该方法考虑了两个方面的因素：服务请求历史和Web服务内部状态。

通过使用这些因素，我们可以有效地预测Web服务未来的QoS。

关键词：Web服务、QoS预测、时间感知、服务请求历史、Web 服务内部状态1. 引言随着Web服务的广泛应用，如何提高Web服务的质量（QoS）已成为业界关注的重点。

QoS预测是提高Web服务质量的有效方法之一。

QoS预测可帮助用户选择高质量的Web服务，以改善系统性能和用户体验。

目前，已有很多QoS预测方法被提出，但这些方法一般只考虑了服务请求历史，忽视了Web服务内部状态对QoS的影响。

除此之外，这些方法还未考虑时间因素，不能够反映Web服务在不同时间段的性能变化。

2. 相关工作2.1 服务请求历史服务请求历史是Web服务QoS预测的一个重要因素，前人已经提出了多种方法来利用服务请求历史预测QoS。

这些方法包括统计、机器学习和深度学习等。

但是，这些方法的预测能力受到服务请求历史的限制。

2.2 Web服务内部状态Web服务内部状态包括服务负载、CPU使用率和内存使用情况等。

这些状态指标可以反映Web服务的当前运行状况，对Web 服务的QoS具有重要作用。

3. 时间感知的Web服务QoS预测方法本文提出了一种时间感知的Web服务QoS预测方法。

该方法考虑了服务请求历史和Web服务内部状态两个方面的因素，以有效地预测Web服务未来的QoS。

3.1 服务请求历史因素在服务请求历史方面，我们采用了一种基于加权滑动窗口的QoS预测方法。

基于QoS关联的Web服务组合算法

基于QoS关联的Web服务组合算法陈彦萍;李翔【期刊名称】《计算机工程》【年(卷),期】2011(037)018【摘要】In order to represent the relation between different Web service classes, service requester's preference as well as internal relation between different Quality of Service(QoS) factors in Web service composition, this paper proposes a determination algorithm of Web service composition based on QoS relation. And in evaluating the quality of service composition, it can consider relations on QoS properties between different service classes to some extend. Result shows algorithm holds good executive efficiency, stability as well as outstanding selecting result.%为反映Web服务组合过程中服务类之间的关联性和客户对服务非功能属性的偏好,以及服务调用过程中不同服务质量(QoS)属性之间的内在关系,提出基于QoS关联的Web服务组合决策算法.在评价服务组合优劣程度的过程中,考虑服务类在QoS属性士的关联关系,对QoS数据进行统一规格化和综合评估.实验征明,该算法拥有较好的执行效率和稳定性,并且选择结果较优.【总页数】3页(P50-52)【作者】陈彦萍;李翔【作者单位】西安邮电学院计算机学院,西安710121;西安邮电学院计算机学院,西安710121【正文语种】中文【中图分类】TP393.03【相关文献】1.基于拓扑序列归约的Web服务组合QoS度量算法 [J], 李兴芳;苑迎春;王克俭2.一种基于QoS的Web服务组合算法 [J], 黄涵;林丕源;黄沛杰;王增钦;张键锋3.基于改进遗传算法的QoS感知Web服务组合 [J], 马小洁;王晓军4.基于全局QoS和免疫粒子群算法的WEB服务组合研究 [J], 肖强华;宁丹;廖颖5.基于QoS的语义Web服务组合优化算法 [J], 王广正因版权原因，仅展示原文概要，查看原文内容请购买。

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