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布拉德福德大学简介

布拉德福德大学University of Bradford学校概况：布拉德福大学于1966年授皇家特许状而成立。

其前身是布拉德福德工学院，创建于1882年，后逐渐发展为纺织、设计和建筑三个学院。

该校经过近一个世纪的发展(尤其是在科学技术领域内的迅速发展)，在1957年已成为一所高科技大学。

多少年来，布拉德福市也一直吸引着来自世界各地的人们，他们中的许多人最后都定居在这里。

来自东欧、印度、巴基斯坦、孟加拉国、西印度群岛和东非的大批居民都慕名而来。

布莱德福大学是一座城市大学，离英国的十大城市之一布莱德福的市中心仅十分钟的步行距离。

从布拉德福市各式各样的商店、饭店和敬神的地方足可以看出该市文化的多样性以及各种文化的融洽性。

该大学在工程、医疗及商务管理等学科拥有雄厚的教学实力。

布莱德福大学以其多元文化教育而自豪，对海外留学生特别重视。

在1997-1998学年，布莱德福大学毕业生的就业率达到创记录的高度，该校79%的毕业生都找到了工作，其就业率在全英大学就业率排名中占前10名。

布莱德福大学的现代语言系为学生开设了令人兴奋的课程，课程具有现代性，实践性和以职业?导向等特点。

同学可学习一门或两门欧洲的语言（法语、德语、俄语和西班牙语）。

布莱德福大学的欧洲研究学位课程是该领域英国首批设立的课程，现代语言系一贯注重研究最新课题并连续发表了高水准的研究成果。

布莱德福大学还开设了跨学科人文研究课程，学生可以将哲学与心理学，社会学和文学等学科结合起来，从而为学生研究人类社会打下坚实的基础，学生在最后一年可以确定专业方向，该课程的研究评估获得高分，教学的评语是“优秀”，这是英国同类课程中唯一获得此评价的课程。

排名情况：2008年《泰晤士》综合和排名第48名开设的课程：1.大学本科预科2.本科学士学位3.研究生预科4.研究生硕士学位5.学术英语课程优势专业：商业及管理、化学及化学工程、土木工程、计算机科学、发展研究、电子及电机工程、机械及制造工程、药学、社会科学(包括经济及妇女研究)。

2022年usnews计算机专业排名

2022年usnews计算机专业排名1、麻省理工学院 Massachusetts Institute of TechnologyMIT的Electrical Engineering & Computer Science(EECS)是多数该领域人士梦寐以求的地方，EECS院系是MIT的工程学院里最大的院系，拥有大概700多名博士学生。

它下面设有四个学位：(1)Master of Science为博士学位之必须阶段，但是学校并不提供最终学位为硕士的学位(2)Master of Engineering仅仅EE，CS自己的本科生可以申请(3)Electrical Engineer and Engineer in Computer Science(4)Doctor of Philosophy and Doctor of ScienceMIT的EE，CS在录取学生的时候，是直接录入到PhD的，学校没有硕士的录取(当然如果最终PhD读不下去了，中途是可以拿到硕士学位的，只要完成了硕士学位的毕业要求)。

于是申请难度就是PhD的申请难度，更别提这所学校在该领域的无人不知无人不晓的深厚的造诣所导致的申请难如登天了。

必须拥有非常深厚的研究潜力和功力方有一丝希望。

2、卡耐基梅隆大学 Carnegie Mellon UniversityCMU是全美乃至全世界最大的计算机学院。

对于一般的美国院校来说，计算机科学只是设置为一个系，即Department of Computer Science, 然而，CMU 对 CS 的建设非常有诚意，直接就开设成为了一个院 School of Computer Science。

研究方向相当全面，研究水平也相当高，你能想到的计算机方面的研究、分支它基本都有，而且还有许多你闻所未闻、十分前沿的研究方向。

根据项目设置的特点，硅谷校区在招生的过程中倾向于软件开发技术过硬、有足够丰富的项目经验的学生。

Queen's University皇后大学本科申请要求及细则

Queen’s University
Queen’s considers applicants only for the programs to which they have applied (except where noted). A maximum of one application per program is permitted. s : H QA QB QIA Subject of major interest (required for upper year). First-year entry only. Upper-year entry possible.
Second Degree Programs Applicants must select a concentration in a substantially new discipline, except for students wishing to upgrade their degree from General to Honours. QAY Second Degree Honours Candidates – Arts s Applied Economics; Art History; Classical Studies; Computing & Creative Arts; Economics; French Studies; Gender Studies; Geography; German; History; Music; Philosophy; Religious Studies; Sociology; Spanish QAW Second Degree Minor Candidates – Arts s Art History; Classical Studies; Computing; Economics; French Studies; Gender Studies; Geography; Geological Sciences; German Language and Literature; History; Italian; Jewish Studies; Mathematics; Music; Philosophy; Physics; Religious Studies; Sociology; Spanish; Statistics; World Languages QDY Second Degree Honours Candidates – Computing s Biomedical Computing; Cognitive Science; Computer Science; Computing; Software Design QDW Second Degree General Candidates – Computing s Computing QSY Second Degree Honours Candidates – Science s Chemistry; Geography; Geological Sciences; Mathematics; Physics; Statistics QSW Second Degree General Candidates – Science s Chemistry; Geography; Geological Sciences; Mathematics; Physics; Statistics Admission Procedure All applicants are required to pay an $85 non-refundable Queen’s University administrative fee to the Ontario Universities’ Application Centre (OUAC). The Ontario Universities’ Application Centre will forward applications to Queen’s Undergraduate Admission. Applicants will be sent an acknowledgement from Queen’s that provides instructions for completing their application. All supporting documentation must be received at Undergraduate Admission before an admission decision can be made. In general, the academic documentation required is: • Secondary school applicants should provide an official transcript of studies completed as well as mid-year marks for current final-year courses. • Applicants who have attended postsecondary institutions should provide an official copy of the secondary and the postsecondary transcripts. To be official, transcripts must bear an original seal or the signature of an administrator at the issuing institution.

Cloud Models and Platforms

Cloud Models and Platforms
Dr. Sanjay P. Ahuja, Ph.D.
Fidelity National Financial Distinguished Professor of CIS
School of Computing, UNF
A Working Definition of Cloud Computing

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 management effort or service provider interaction.

Pay-per-use: Pay only for what you use
Multitenancy: Ability to have multiple customers access their servers in the data center in an isolated manner
This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.

Formal Aspects of Computing (2005) Formal Aspects of Computing

DOI 10.1007/s00165-005-0062-0BCS ©2005Formal Aspects of Computing (2005)Formal Aspects of Computing Using probabilistic model checking for dynamic power managementGethin Norman 1,David Parker 1,Marta Kwiatkowska 1,Sandeep Shukla 2and Rajesh Gupta 31School of Computer Science,University of Birmingham,Birmingham,UK2BradleyDepartment of Electrical and Computer Engineering,Virginia Tech,Blacksburg,USA3Department of Information and Computer Science,University of California,San Diego,USA Abstract.Dynamic power management (DPM)refers to the use of runtime strategies in order to achieve a trade-off between the performance and power consumption of a system and its components.We present an approach to analysing stochastic DPM strategies using probabilistic model checking as the formal framework.This is a novel application of probabilistic model checking to the area of system design.This approach allows us to obtain performance measures of strategies by automated analytical means without expensive simulations.Moreover,one can formally establish various probabilistically quantiﬁed properties pertaining to buffer sizes,delays,energy usage etc.,for each derived strategy.Keywords:Power management;Formal methods;Embedded systems;Model checking;Probabilistic model checking1.IntroductionThe nature of computing has been changing over the last few years from server,workstation and desktop based computing to embedded,ubiquitous and pervasive computing.Handheld devices,wireless sensors and biomed-ical devices are gaining more and more prominence in all arenas of human life.However,as we move from the wired to wireless domain,power savings in these computing devices become more crucial.As a result,much research has been done in the area of low-power design,power management and the balance between computa-tion and communication power.Each kind of approach to power savings has its own limitations.For example,circuit-level or architecture-level mechanisms cannot take advantage of application characteristics.As a result,system-level power management,which is characterised by operating system controlled power saving measures based on the observation of application characteristics,has gained signiﬁcant attention in the last few years.There are two distinct ﬂavours of system-level power management:dynamic voltage/frequency scaling (DVS/DFS)and dynamic power management (DPM).In this paper,we focus on the latter.Dynamic power management (DPM)is a way to save energy in devices which,under operating system con-trol,can be switched either on and off or between several power states of varying power consumption.DPM has gained considerable attention over the last few years,a trend evidenced in the research literature [HAW96,Correspondence and offprint requests to :Gethin Norman,School of Computer Science,University of Birmingham,Birmingham B152TT,UK.Email:G.Norman@G.Norman et al. SCB96,BM98,BMM01,SG01,RIG00,CBBM99,ISG02],as well as concerted industry efforts such as Micro-soft’s OnNow[Mic98]and ACPI[ACP].Due to the importance of the minimising power consumption in today’s embedded systems,a lot of work has been initiated in both the component manufacturing industry and the systems design industry.A survey of most of the techniques developed for DPM before2000can be found in[BBM00].In this extensive review,the approaches to DPM are classiﬁed into predictive schemes and stochastic optimum control schemes. Predictive schemes attempt to predict a device’s usage behaviour in the future,typically based on the past his-tory of usage patterns,and decide to change power states of the device accordingly.Stochastic approaches make probabilistic assumptions(based on observations)about usage patterns and exploit the nature of the probability distribution to formulate an optimisation problem,the solution to which drives the DPM strategy.It has been noted that predictive schemes are mostly based on devices with two power-saving states,whereas there are many instances of devices in the embedded world which have more than two states.Examples of such devices may be found in[BBM00,SBM99].In order to provide DPM strategies for multi-state systems,the sto-chastic optimum control approach has been proposed in the literature[BBM00,SBM99,BBPM99,CBBM99, QP99,QWP01].However,such stochastic approaches also have their drawbacks,including the fact that they make many assumptions about the probabilistic nature of the inputs and may be more computationally expensive to implement.Much of the previous work on dynamic power management has been based on ad-hoc techniques,such as the use of regression equations,interpolation or learning based methods.Stochastic approaches tend to be more formal in the sense that they are based on mathematical models which make precise assumptions about the prob-abilistic characteristics of,for example,when service requests arrive at a device and how long the device takes to respond to these requests.Validation and analysis of the stochastic DPM schemes,however,is less formal,eval-uation usually being carried out with simulation techniques which are time consuming and often not completely reliable.In this paper,we illustrate the applicability of probabilistic model checking,an automatic formal veriﬁcation technique for the analysis of systems which exhibit probabilistic behaviour,to the area of dynamic power man-agement.We show how the probabilistic model checking tool PRISM[KNP04,Pri]can be used to automatically provide a detailed comprehensive of stochastic DPM schemes.Furthermore,this analysis is more accurate than that obtained by simulation which typically yields only average case behaviour.An earlier version of this work appeared in[NPK+02,NPK+03].anisationSection2introduces the stochastic approach to DPM,with references to the existing literature.Section3describes the basics of probabilistic model checking and the PRISM tool.Section4shows in detail how probabilistic model checking and PRISM have been applied to the analysis of DPM strategies and together with MAPLE[Map] used to generate DPM strategies.Finally,Sect.5concludes the paper.2.Stochastic approaches to DPMThe stochastic version of the DPM problem basically requires one to devise a strategy(policy)which may be probabilistic,in the sense that the actions to be taken by the strategy may have probabilities attached to them. Unlike deterministic strategies,where a particular state of the system will lead the strategy to take a deterministic action,here the strategy can choose between multiple actions with pre-designated probabilities.In recent years,several approaches for designing stochastic DPM strategies have been proposed[PBBM98, BBPM99,BBM00,CBBM99,QP99,QWP00,QWP01,SBM99].These methodologies are based on a stochastic model of the DPM problem,which incorporates the probabilistic characteristics of request arrivals to the device, the device response time distribution,the power consumption by the device in various states and the energy consumption when the device changes state.From this stochastic model,an exact optimisation problem is for-mulated,the solution to which is the required optimal stochastic DPM policy.The strategy devised must ensure that power savings are not achieved at an undue cost in performance.One approach,for example,is to construct a policy which optimises the average energy usage while bounding average delay.The constructed policies are usually validated by simulation to check for the soundness of the modelling assumptions,and the effectiveness of the strategies in practice[QP99,PBBM98].Using probabilistic model checking for dynamic power managementThe stochastic models which have been used in the literature are discrete-time Markov chains [PBBM98,BBPM99],continuous-time Markov chains [QP99,QWP00,QWP01]and their variants [SBM99].The approaches vary in the modelling of time:in the continuous-time case,mode switching commands can be issued at any time,and events can happen at any time.In the discrete-time case,all events and actions occur at certain discrete time points.In practice,such stochastic modelling seems to work well for speciﬁc kinds of applications.Generally,the stochastic matrices for these models are created manually.In [QWP00],stochastic Petri nets are used,which allows automatic generation of the stochastic matrices and formulation of the optimisation problems.3.Probabilistic model checkingModel checking is a well established and successful technique for the automatic veriﬁcation of ﬁnite state sys-tems.In recent years,a signiﬁcant amount of work has gone into probabilistic model checking ,which allows for veriﬁcation of systems that exhibit probabilistic behaviour.These include randomised algorithms,which use probabilistic choices or electronic coin ﬂipping,and unreliable or unpredictable processes,such as fault-tolerant systems or communication networks.To perform probabilistic model checking one ﬁrst constructs a probabilistic model of the system under study.As in the non-probabilistic case,this model is usually a labelled transition system which deﬁnes the set of all possible states that the system can be in and the transitions which can occur between these states.However,in this case,one must also augment the model with information about the likelihood that each transition will take place.Properties of the system which are to be veriﬁed are then speciﬁed,typically in probabilistic extensions of temporal logic.These allow speciﬁcation of properties such as:“shutdown occurs with probability at most 0.01”;or “the video frame will be delivered within 5ms with probability at least 0.97”.A probabilistic model checker applies algorithmic techniques to analyse the state space of the probabilistic model and determine whether these speciﬁcations are satisﬁed.Typically,this involves computation of one or more probabilities or performance measures.The operations required are graph-based analysis and solution of linear equation systems or linear optimisation problems.3.1.Probabilistic modelsModels used in probabilistic model checking are commonly variants of Markov chains.The simplest is dis-crete-time Markov chains (DTMCs).A DTMC is deﬁned by a set of states S and a probability transition matrix P :S ×S →[0,1],where s ∈S P (s,s ) 1for all s ∈S .This gives the probability P (s,s )that a transition will take place from state s to state s .Continuous-time Markov chains (CTMCs)extend DTMCs by allowing transitions to occur in real-time,rather than only in discrete steps.A CTMC is deﬁned by a set of states S and a transition rate matrix R :S ×S →I R 0.The rate R (s,s )deﬁnes the delay before which a transition between states s and s is enabled.The delay is sampled from a negative exponential distribution with parameter equal to this rate,i.e.the probability of the transition being enabled within t time units is 1−e −R (s,s )·t .When R (s,s )>0for two target states,a race occurs and the transition which becomes enabled ﬁrst is the one taken.Exponentially distributed delays are often suitable for modelling component lifetimes and inter-arrival times.They can also be used to approximately model more complex probability distributions.3.2.Analysis of probabilistic modelsSimilarly to the conventional,non-probabilistic case,probabilistic model checking usually constitutes verifying whether or not some temporal logic formula is satisﬁed by a model.The two most common temporal logics for this purpose are PCTL [HJ94,BdA95]and CSL [ASSB96,BKH99],both extensions of the logic CTL.PCTL is used to specify properties for DTMCs and MDPs;CSL is used for CTMCs.One common feature of the two logics is the probabilistic operator P ,which allows one to reason about the probability that executions of the system satisfy some property.For example,the formula P 1[ terminate ]states that,with probability 1,the system will eventually terminate.On the other hand,the formula P 0.95[¬repair U 200terminate ]asserts that,with probability 0.95or greater,the system will terminate within 200time units andG.Norman et al. without requiring any repairs.These properties can be seen as analogues of the non-probabilistic case,where a formula would typically state that all executions satisfy a particular property,or that there exists an execution which satisﬁes it.CSL also provides the S operator to reason about steady-state(long-run)behaviour.The for-mula S<0.01[queue size m ax],for example,states that,in the long-run,the probability that a queue is full is less than0.01.Strictly speaking,probabilistic speciﬁcations in PCTL and CSL(such as the examples above)always contain a probability bound,so that properties are either true or false for a given system.In practice,however,this can be relaxed.Model checking algorithms for PCTL and CSL typically proceed by computing the actual probability and then comparing it to the bound.Hence,in practice,we can write an expression of the form P ?[ terminate], for which the model checker will return the actual probability that the system terminates.In many cases,the most useful form of analysis is to compute such values for a range of models or properties.For example,one might determine P ?[ t terminate]for a range of values of t in order to gain insight into the likelihood of the system terminating as time progresses.Further properties can be analysed by introducing the notion of costs(or,conversely,rewards).If each state of the probabilistic model is assigned a real-valued cost,we can compute properties such as the expected cost to reach certain states,the expected accumulated cost over some time period,or the expected cost at a particular time instant.As in the previous paragraph,such properties can also be expressed concisely and unambiguously in temporal logic[dA97,BHHK00].3.3.PRISM:a probabilistic model checkerPRISM[KNP04,Pri]is a probabilistic model checker developed at the University of Birmingham.It supports analysis of the two types of probabilistic models discussed previously:discrete-time Markov chains(DTMCs) and continuous-time Markov chains(CTMCs),and also Markov decision processes(MDPs)which we do not use here.It veriﬁes properties speciﬁed in the temporal logics PCTL(for DTMCs and MDPs)and CSL(for CTMCs).Other probabilistic model checkers include ProbVerus[HGCC99]for DTMCs,E T M C2[HKMKS00] for CTMCs and DTMCs,and RAPTURE[JDL02]for MDPs.PRISM has been used to analyse a wide range of case studies,including probabilistic algorithms for problems such as anonymity,contract signing,leader election and consensus;and performance analysis of various queue-ing systems,communication networks and manufacturing systems.See[Pri]for further details.Figure1shows a screenshot of the tool running.Probabilistic models to be analysed in PRISM are speciﬁed in the PRISM language,which is based on the Reactive Modules formalism of Alur and Henzinger[AH99].The basic components of this language are modules and variables.A system is constructed as the parallel composition of a set of modules.A module contains a number of variables which express the state of the module.Its behaviour is given by a set of guarded commands of the form:[]<guard>→<command>;The guard is a predicate over all the variables of the system and the command describes a transition which the module can make if the guard is true.A command is speciﬁed by deﬁning the new values of the variables of that module.This means that a module can read all of the variables in the system but only write to its own local variables.In general,the behaviour of a module is probabilistic,in which case a command takes the form: <prob>:<action>+···+<prob>:<action>where<prob>is a probability when the model is a DTMC or MDP and a non-negative,real value(taken to be the parameter of an exponential distribution)when it is a CTMC.In addition,the pair of square brackets at the start of a guarded command can contain a label.Actions from different modules with the same label take place synchronously.See[Pri,Par02]for more details.The overall functionality of the PRISM tool is as follows.First,it reads and parses a model description in the PRISM language.It then constructs the corresponding DTMC,CTMC or MDP,computes the set of all reachable states,and identiﬁes any deadlock states(i.e.reachable states with no outgoing transitions).If required, the transition matrix of the probabilistic model constructed can be exported for use in another tool.Typically, though,PRISM then parses one or more properties in PCTL or CSL and performs model checking,determining whether the model satisﬁes each property.A prototype version of PRISM has also been developed which supports model checking of cost and reward related properties,as described in the previous section.Using probabilistic model checking for dynamic power managementFig.1.Screenshot of the PRISM graphical user interface4.Probabilistic model checking and DPMIn this section,we describe how probabilistic model checking and,in particular,PRISM can be applied to the analysis of DPM strategies obtained using the approaches of[QP99,PBBM98].These approaches are based on constructing a probabilistic model of the dynamic power management system from which,for a given con-straint,an optimisation problem is constructed.The solution to this problem is the optimum randomised power management strategy satisfying this constraint.We show how PRISM can be used to construct a probabilistic model of dynamic power management.The corresponding optimisation problem(as described in[QP99,PBBM98])is then solved with the symbolic solver MAPLE[Map].Finally,we again use PRISM to automatically validate and analyse the derived policies.Note that we use MAPLE to solve the optimisation problem because PRISM does not currently support methods for solving problems of this type.1This approach differs from the previously employed techniques for the validation and analysis of DPM strate-gies,which rely on simulation or the actual implementation of the schemes in device drivers.The advantage of the probabilistic model checking approach is that it avoids the higher cost of simulation and beneﬁts of detailed anal-ysis before deployment in hardware.Furthermore,the analysis is more accurate than that obtained by simulation which typically yields only average case behaviour.1PRISM does support the solution of the optimisation problems generated when verifying MDPs.However,these problems are speciﬁc instances of the Stochastic Shortest Path Problem[BT91,Ber95]and,since the solution techniques employed by PRISM rely on this fact, these methods cannot be applied to general optimisation problems.G.Norman et al.Table1.Average power consumption(W)and service times(ms)for each power stateSleep Standby Idlelp Idle ActivePower(W)0.10.30.8 1.5 2.5Service time(ms)00001Table2.Average transition times(ms)between power statesActive Idle Idlelp Standby SleepActive–15220600Idle1–5220600Idlelp5––220600Standby220–––600Sleep600––––4.1.Modelling DPM in PRISMWe have applied probabilistic model checking to two stochastic DPM approaches:that of Benini et al.[PBBM98, BBPM99],based on discrete-time Markov chains,and that of Qiu et al.[QP99,QWP00,QWP01],based on continuous-time Markov chains.In this section we describe the DTMC approach in detail.The approach is described through the example of[PBBM98,BBPM99],an IBM TravelStar VP disk-drive [IBM].The device hasﬁve power states,labelled sleep,standby,idle,idlelp and active.It is only in the state active that the drive can perform data read and write operations.In state idle,the disk is spinning while some of the electronic components of the disk drive have been switched off.The state idlelp(idle low power)is similar except that it has a lower power dissipation.The states standby and sleep correspond to the disk being spun down. Tables1and2show actual data for these power states.Table1gives the average power consumption(W)and the service time(ms)for each state.Table2shows the average time(ms)to transition between each pair of states.We now describe how the system is modelled in the PRISM language.Following the approach of[PBBM98, BBPM99],the model constructed is a discrete-time Markov chain(DTMC).Based on the fastest possible tran-sition performed by system,we choose a time resolution of1ms for the model,i.e.each discrete-time step of the DTMC will correspond to1ms.The basic structure of the DPM model can be seen in Fig.2.The model consists of:a Service Provider(SP), which represents the device under power management control;a Service Requester(SR),which issues requests to the device;a Service Request Queue(SRQ),which stores requests that are not serviced immediately;and the Power Manager(PM),which issues commands to the SP,based on observations of the system and a stochastic DPM policy.Each component is represented by an individual PRISM module,which we now consider in turn.4.1.1.Modelling the power manager(PM)The PM decides to which state the SP should move at each time step.To model this,we split each step into two parts:in theﬁrst,the PM(instantaneously)decides what the SP should do next(based on the current state);andUsing probabilistic model checking for dynamic power managementmodule CLOCKc:[0..1]init0;[tick]c 0→(c 1);[tock]c 1→(c 0);endmoduleFig.3.PRISM module for the clockmodule PMpm:[0..4];//0−go to active,1−go to idle,2−go to idlelp,3−go to standby,4−go to sleep[tick]cond1→prob active1:(pm 0)+prob idle1:(pm 1)+prob idlelp1:(pm 2)+prob standby1:(pm 3)+prob sleep1:(pm 4);[tick]cond2→prob active2:(pm 0)+prob idle2:(pm 1)+prob idlelp2:(pm 2)+prob standby2:(pm 3)+prob sleep2:(pm 4);...endmoduleFig.4.PRISM module for the power managerin the second,the system makes a transition(with the SP’s move based on the choice made by the PM).To achieve this,we introduce the CLOCK module,given in Fig.3.Transitions of this module are labelled alternately with tick and tock.The PM is then constructed to synchronise with the CLOCK on tick,while the remaining compo-nents are constructed to synchronise with the CLOCK on tock.A generic PM has the form given in Fig.4.For example,if the state of the system satisﬁes cond1,then the PM decides that with probability prob active1the SP will move to active,with probability prob idle1the SP will move to idle,with prob idlelp1to idlelp,prob standby1 to standby,and prob sleep1to sleep.4.1.2.Modelling the service provider(SP)As mentioned above,the SP(the disk drive)hasﬁve power states(active,idle,idlelp,standby and sleep).These states and the possible transitions between them are shown in Table2.The actual PRISM code is shown in Fig.5. Recall that the SP synchronises with the clock on tock.Hence,all of its guarded commands are labelled with this action.Note also that the behaviour of the SP depends on the PM,so the guards reference the variable pm.Since a time resolution of1ms has been chosen,in order to correctly model transitions with delays longer than this time resolution transient states are introduced.For example,the transient state active idlelp is used to model the non-unitary time transition from active to idlelp.The transition probabilities in the transient states, taken directly from the data of[PBBM98,BBPM99],are chosen such that the mean times to move between power states are as given in Table2.Note that we suppose that the power dissipation in these transient states is high (2.5W).4.1.3.Modelling the service requester(SR)and queue(SRQ)Similarly to the SP,both the SR and the SRQ synchronise with the clock on tock.The SR has two states:idle where no requests are generated and req where one request is generated per time step(1ms).The probabilities associated with the transitions between these states are based on time-stamped traces of disk access measured on real machines[BBPM99].The module for the SR is given in Fig.6.The SRQ models a queue of service requests.It responds to the arrival of requests from the SR and the service of requests by the SP.The queue size will only decrease when the SR and SP are in states idle and active,G.Norman et al.module SPsp:[0..10]init9;//0 active,1 idle,2 active idlelp,3 idlelp,4 idlelp active,5 active standby//6 standby,7 standby active,8 active sleep,9 sleep,10 sleep active//states where PM has no control(transient states)[tock]sp 2→0.75:(sp sp)+0.25:(sp 3);[tock]sp 4→0.75:(sp sp)+0.25:(sp 0);[tock]sp 5→0.995:(sp sp)+0.005:(sp 6);[tock]sp 7→0.995:(sp sp)+0.005:(sp 0);[tock]sp 8→0.9983:(sp sp)+0.0017:(sp 9);[tock]sp 10→0.9983:(sp sp)+0.0017:(sp 0);//PM:goto active[tock]pm 0∧(sp 0∨sp 1)→(sp 0);[tock]pm 0∧sp 3→(sp 4);[tock]pm 0∧sp 6→(sp 7);[tock]pm 0∧sp 9→(sp 10);//PM:goto idle[tock]pm 1∧(sp 0∨sp 1)→(sp 1);[tock]pm 1∧(sp 3∨sp 6∨sp 9)→(sp sp);//PM:goto idlelp[tock]pm 2∧(sp 0∨sp 1)→(sp 2);[tock]pm 2∧sp 3→(sp sp);//PM:goto standby[tock]pm 3∧(sp 0∨sp 1∨sp 3)→(sp 5);[tock]pm 3∧sp 6→(sp sp);//PM:goto sleep[tock]pm 4∧(sp 0∨sp 1∨sp 3∨sp 6)→(sp 8);[tock]pm 4∧sp 9→(sp 9);endmoduleFig.5.PRISM module for the service providermodule SRsr:[0..1]init0;//0-idle and1-req[tock]sr 0→0.898:(sr 0)+0.102:(sr 1);[tock]sr 1→0.454:(sr 0)+0.546:(sr 1);endmoduleFig.6.PRISM module for the service requesterrespectively.On the other hand,it will only increase when the SR is in state req and the SP is not active.The PRISM code is given in Fig.7.4.1.4.Modelling aﬁnite time horizonWe suppose that there is a time horizon of one million time steps.To model this horizon,an additional module representing a battery with an expected life span of1million time steps is added(see Fig.8).Note that,once the battery reaches state0,it cannot perform the action tock which prevents any other modules in the system from performing this action.Hence,the rest of the system can no longer continue(i.e.the states where bat 0act as sink states).4.2.Policy constructionUsing the PRISM language description detailed in the previous sections,the PRISM model checking tool can be used to construct a generic model of the power management system.From the transition matrix of this sys-tem,the linear optimisation problem whose solution is the optimal policy can be formulated,as described inUsing probabilistic model checking for dynamic power managementconst QMAX 2;//maximum size of the queuemodule SRQq:[0..QMAX]init0;//size of queue//SP is active[tock]sr 0∧sp 0→(q max(q−1,0));[tock]sr 1∧sp 0→(q q);//SP is not active[tock]sr 0∧sp>0→(q q);[tock]sr 1∧sp>0→(q min(q+1,QMAX));endmoduleFig.7.PRISM module for the service request queuemodule BATTER Ybat:[0..1]init1;//0-battery off and1-battery on[tock]bat 1→0.999999:(bat 1)+0.000001:(bat 0);endmoduleFig.8.PRISM module for the battery[PBBM98,BBPM99].This optimisation problem is then passed to the MAPLE symbolic solver.Table3shows policies constructed in this way for a range of constraints on the average size of the service request queue.The ﬁrst column lists the constraint;the second column summarises the corresponding policy.4.3.Policy analysisOnce a policy has been constructed,its performance can be automatically analysed through probabilistic model checking,as described in Sect.3.The generic power manager PRISM module is modiﬁed to represent a speciﬁc policy.Figure9shows an example of this for the constraint“queue size is less than0.05”.This can be seen to correspond to the policy in the5th row of the table in Table3.PRISM is then used to construct and analyse the DTMC for this policy.We now present a representative set of results obtained through probabilistic model checking that demonstrate the utility and power of this approach.The policies analysed are those constructed from a range of constraints on the average queue length.In Table4,the following properties have been computed:“average power consumption”,“average queue size”and“average number of lost requests”.Using PRISM,we associate a cost with each state and then compute the expected accumulated cost of the system until it reaches a state where the battery has run out.For example,to determine the average power consumption,the cost associated with each state is determined by the state of the SP and the data given in Table1.From the table,we can see that the average power consumption of a policy decreases as the constraint on queue size is relaxed(i.e.the requested average queue size is increased).We can also validate the policy by conﬁrming that the expected size of the queue matches the value in the constraint which was used to construct it.Finally,we see that a side-effect of this is that the average number of requests lost also increases.In Fig.10,we show graphical results for a range of ing the same assignments of model states to costs as discussed above,we(automatically in PRISM)compute and plot,for a range of values of T:“expected power consumption by time T”,“expected queue size at time T”,and“expected number of lost requests by time T”.Theﬁrst and third properties are determined by computing expected cost cumulated up until time T;the second by computing the instantaneous cost at time T.Again,we see that policies which consume less power have larger queue sizes and are more likely to lose requests.Here,though,we can get a much clearer view of how these properties change over time.We see,for example,that the expected queue size at time T initially increases and then decreases.This follows from the fact that the strategies wait for the queue to become full before switching the SP on.。

美国计算机博士院校申请的难度在哪里

美国计算机博士院校申请的难度在哪里美国计算机专业霸主的位置在世界范围内都是无可撼动的，计算机专业强烈的竞争环境，也使得美国计算机博士的申请看似迷雾重重，下面跟86店铺小编一起来看一下美国计算机博士院校申请到底有多大的难度。

超级难申超级优秀超级全面系列：Stanford/UCBerkeley/MIT这三个学校在research上非常全面，几乎没有弱项。

尤其是MIT 的工作，相当有impact。

这三个学校reputation相当优秀，申请难度很大。

细说的话，MIT稍微比Stanford和Berkeley好申一些，Stanford最难。

对于MIT/Stanford，我私认为是光凭自身硬件搞不定的(可能THU除外)。

我听说过的所有国内学生申请成功的例子，除了自身硬件牛以外，都是由有internationalreputation的prof(很多还不止一个)强力推荐。

当然，这两个条件本身就有很大的关联性。

超级难申超级优秀系列：Caltech/Harvard没错，排在第二档就是Caltech和Harvard这两个USnews十名开外的学校。

这两个学校无论从 reputation，faculty质量还是申请难度来说，绝对和上面三个不相上下。

但是由于department比较小，所以ranking不是很高。

Caltech总共只有15个prof，可以说是每个人独当一面。

Harvard的强项是theory，不够全面。

但是，如果你的运气好到在这两个学校刚好有match的prof的话，那么，如果没有上面三家的offer，我建议优先考虑这两家。

非常难申非常优秀超级全面系列：CMUCMU可以说是计算机类学校中的一枝奇葩，schoolof computer science下6个department。

Research相当全面，水平也很高，尤其是AI相关的方向。

CMU因为department比较大，招的人相对多，申请难度比上面5所小不少。

软件工程英文参考文献(优秀范文105个)

当前,计算机技术与网络技术得到了较快发展,计算机软件工程进入到社会各个领域当中,使很多操作实现了自动化,得到了人们的普遍欢迎,解放了大量的人力.为了适应时代的发展,社会各个领域大力引进计算机软件工程.下面是软件工程英文参考文献105个，供大家参考阅读。

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Dear professor

Dear professor,I have applied the Software Engineering(meng) of the school of Computing and Software. I write the email to ask if you could be my supervisor. My name is Li Xu and I have specialized in Software Engineering at Wuhan University of Technology from September 2012 till present. I will obtain my Bachelor’s Degree of Engineering in June 2016.I have been always interested in the area of Information privacy and security and I have delved myself into this realm since I entered university(要是可以加一点自己的研究的话可能就更好了). Nowadays, just as people can’t live without air,people almost can’t live without the Internet. However, at the same time, people’s personal privacy is easier to be given away, which has been a serious problem. And I believe that ,in the near future, everyone of us would pay more attention on this issue with the fast development of computer technology.I have red your papers about eHealth, and all of them ,especially the one about the M4CVD,imposed indelible impressions on me. It’s pretty brilliant to get the input data from both the physiological signals measured using wearable sensors and clinical data form health records. But I have a question that if this system could download all input data and store in the local device and work without Internet connection, as it’s not so easy for people to have access to free and safe WIFI when they are outside.I hope that I will have the chance to work together with you to further explore this problem in the future.Thanks for your attention and look forward to hearing from you.Sincerely,LI XU。

电脑用于学校教育中英文

电脑用于学校教育中英文电脑用于学校教育中英文例文Computer Use in School EducationAccompanying the developments in computing as a subject for study th Accompanying the developments in computing as a subject for study there has been a corresponding growth in the use of the computer as an aid to teaching across the curriculum. The government offer of half-price computers led to the installation of a large number of school microcomputer systems at a time when there was very little educational software. At the same time there was an explosive demand for introductory courses, at first for secondary teachers and later, when the offer was extended to primary schools, for primary teachers. It would be impossible, and inappropriate, to make every teacher into a computer programming expert.What the teacher needs to know is how to connect up a system. And how to load and run programs. Once these skills have been acquired the much more important topic of the evaluation of. computer-based teaching materials can be addressed.The Unintelligent MachineOver the past 20 years the amount of computing power available for a given sum of money has approximately doubled every two years, and it looks as if this trend will continue in the foreseeable future. On the other hand, the fundamental logical design of computers is much the same as at the beginning of this period. The revolution has been one of scale and cost rather than a change in the kinds of things which computers can do. One might have expected therefore that by now we would know thebest way in which computers can be used to help with the educational process.。

英国布拉德福德大学商学院介绍

布拉德福德大学是一所具有140多年悠久历史的英国老牌综合性公立大学， 1966年根据英国皇家宪章正式命名为布拉德福德大学。

校园内有一流的教学设施以及配套的图书馆、计算机中心、书店、体育中心、洗衣房、商店、酒吧、餐厅、电影院及娱乐场所。

布拉德福德大学拥有两个校区，主校区距离市中心步行仅需5分钟，而管理学院校区位于距离主校区约3公里处，其环境优美、建筑古老、教学设备先进、教学水平世界闻名。

布拉德福德大学商学院成立的时间比大学成立的时间还要早，是英国为数不多的同时受AMBA，EQUIS，和AACSB认证的商学院之一，其中商学院在英国位于第八名。

并且学校毕业生的就业率非常高，位于当地第一。

立思辰留学360表示，商学院中国际学生的比例高达70%多，但是中国学生的数量并不是很多，只占其中的5%。

一、学院设置1.管理学院School of Management (including Law)(被认证AMBA、AACSB、EQWIS. 专业排名世界百强，欧洲TOP20，北英TOP8)2.信息工程学院School of Computing， Informatics and Media3.设计、工程技术学院(School of Engineering， Design and Technology)4.生命科学School of Life Sciences5.健康研究学院School of Health Studies6.教育和发展学院School of Lifelong Education and Development (SLED)7.社会及国际问题研究学院School of Social & International Studies二、学校优势布拉德福德大学与世界大量的企业合作，实习机会广。

课程设置专注于实践操作经验，所以每年毕业生就业率很高。

Gmat可以用学校在线内部测试代替，由两部分构成笔试、口试，每部分20分钟。

大学中各学院的英文翻译

在行政职务中，assitant指“助手”，可译作“副”或“助理”，即正职的主要助手，如：部长助理——assistant minister, 副司令员——assistant commander,公司副经理——assistant manager,副校长（中小学）——assistant headmaster;在技术职称中，assistant指“助理”，如助理工程师——assistant engineer,助理农艺师——assistant agronomist,助理会计师——assistant accountant,助理巡视员——assistant counsel。
deputy与director, chief, head secretary, dean, mayor搭配；
sub-与commissioner, dean, head, chief, editor, master, chairman, principal搭配。
一些不符合英语习惯的搭配要防止，如：vice-professor, vice-director, vice-manager, vice-mayor, vice-mayor, vice-editor-in-chief.
大学里的副教授一般用associate professor,不用vice-professor或deputy professor,同样的例子如：副研究员——associate research fellow,副主编——associate editor,副审判长——associate chief judge,学院的副院长、大学的副教务长——associate dean。
◆ School of Mines矿院
◆ School of Safety Engineeranics & Civil Engineering力建

外文翻译---套接字缓冲区的TCP自动调谐守护进程

A TCP Socket Buffer Auto-tuning DaemonShao TaoSchool of Computing National University of Singapore Email:shaot@.sgLillykutty JacobSchool of ComputingNational University of SingaporeEmail:jacobl@.sgA.L.AnandaSchool of ComputingNational University of SingaporeEmail:ananda@.sgAbstract—Depending on the bandwidth and round trip time of a network path,certain network connections may need different socket buffers than others.However,most TCP implementations use default socket buffer size for all the connections.This leads to a wastage of kernel memory for low bandwidth,long round trip time network paths and inefﬁciency for long fat[1]network paths.To tackle this problem,we implement a TCP socket buffer auto-tuning daemon.It will periodically tune the socket buffer size of each connection to an optimal value according to the network status of each connection,without the intervention of user or change of network application.The tuning daemon can run on different machines and communicate with each other,send tuning request to the remote daemon and exchange the tuning information.Experiment results are presented to show that this daemon can improve the throughput for network application.Index Terms—TCP,network throughput,round trip time, socket buffer,auto-tuning,bandwidth-delay product,perfor-mance tuningI.I NTRODUCTIONThe most frequently used parameter for determining the net-work performance is the throughput,especially for application involving large amount of data work throughput is affected by various factors,including network hardware,cross trafﬁc,operating system of the ending hosts,application being used,and most commonly-the conﬁguration of the hosts.In order to take advantage of today’s high-speed networks,hosts must support and utilize some extensions to basic TCP/IP. The most common ones are Path MTU Discovery[2],TCP Window Scale Option[3],TCP SACK[4]and large socket buffers.Among all the four new features mentioned above,the ﬁrst three can be easily turned on or off.However the socket buffer size is connection speciﬁc.For each TCP connection, we should use different socket buffer size according to a set of network parameters and host’s speciﬁcations.The best solution would be if the operating system can automatically tune the socket buffers to the optimal size[5].However,no procedure or system is place to do the same.The TCP socket buffer tuning daemon addresses the issue of how socket buffer size can affect throughput of network and how it can be tuned to be optimal for bulk data transfer,au-tomatically(without having to manually determine the optimal value and changing accordingly).Section II introduces some of the related works.In Section III we will look at the design of our tuning daemon and some of the algorithm used to determine the optimal socket buffer size.Section IV contains experiment results while conclusions and recommendations for future work are given in Section V.II.R ELATED W ORKSWeb100project[6]was established with the aim of running network application at“100%of the available bandwidth”. This project provides a Linux kernel2.4patch and shared library for application development.Jeffrey Semke,Jamshid Mahdavi,and Matthew Mathis’paper[7]introduced their techniques of socket buffer tuning and implementation in a NetBSD system.They modiﬁed the kernel to ensure the fair share of kernel memory between network connections.The National Laboratory for Applied Network Research(NLANR) has developed their auto-tuning enabled ftp client and server [8].The client estimates the bandwidth-delay product of the network path,by using the packet pair[9]scheme.The client uses the estimated bandwidth-delay product value as the socket buffer size for that connection.Brian L.Tierney and his colleagues in Lawrence Berkeley National Laboratory developed their Enable daemon[10]and a corresponding set of APIs.The applications can query the Enable daemon for the optimal socket buffer size to use for a certain destination host. Eric Weigle has done a detailed comparison[11]over seven buffer tuning techniques,including their features and perfor-mance comparison.According to the classiﬁcation method in that paper,our socket buffer tuning daemon is kernel-level, dynamic,out-of-band and transparent.Compared with previous works,the socket buffer auto-tuning daemon presented in this paper is a dynamic kernel-level implementation that uses active probing.The probing daemon is running at user-level and also has the ability to communicate between different instances for tuning informa-tion exchange,which makes it highly portable and extensible.III.D ESIGN I SSUESA.Design CriteriaSeveral issues need to be considered when designing the tuning daemon.The tuning daemon should be time efﬁcient. Each round of probing operation must beﬁnished within three seconds;or else,it should be canceled.Memory usage of the tuning daemon should be carefully handled.The tuning daemon runs in user space,and it needs only20KBytes user space memory.The CPU time spent on the tuning daemon should be low.When the tuning daemon runs,the CPU usage for the daemon process is only0.6%percent on our Pentium R 866MHz work bandwidth required for the prob-ing and communication operation should be controlled-thenetwork bandwidth occupied by the tuning daemon is15 KBytes/second within short probing period and none during delay period.Our tuning daemon runs the probe at adjustable intervals,normally60-300seconds and probing period lasts for around one minute for each different network connection. Finally the tuning daemon should be easily portable to other UNIX like systems.B.Algorithms and SchemesThere are two major functional parts in the tuning daemon.1)Detection of TCP Connections:For detection of local TCP connection,we make use of the/proc system in Linux. Information about all the local TCP connections is listed in the ﬁle/proc/net/tcp.Each line in thisﬁle contains statistics of one single TCP connection.The main parameters are:sl, local address,rem address,st,uid,and inode.Table I explains the meaning of theseﬁelds.Our tuning daemon will periodically read thisﬁle to retrieve the statistics of the currently active TCP connections and tune these connections.TABLE IF IELDS OF/P R O C/N E T/T C PField Name Meaningsl sequence numberlocal address local IP address:local port numberrem address remote IP address:remote port numberst TCP connection statusuid id of the owner of that connectioninode inode number of the local socket.2)Measurement of Bandwidth:pipechar[12]is used to measure the network bandwidth.During each round of prob-ing,four probe packets,P1,P2,P3and P4are sent out.The probe packets P1and P3are UDP packets whose destination ports are discard.Packets P2and P4are small UDP packets whose destination ports are unreachable.P1has a data size of S1bytes and P3has a data size of S3.The data portion size of P2and P4is1byte.For a general case,suppose the size of P2and P4is S.P1and P2are sent back-to-back to the probed destination host.P1will be received and ignored by the destination host. P2will generate an ICMP Port Unreachable error message at destination host and this message will be returned to sender. T1is used to represent the time between the moment when sending out P1plus P2and the moment when receiving the ICMP error message caused by P2.Likewise,P3and P4are sent out back-to-back.P3will be received and ignored by the probed host and P4will generate an ICMP Port Unreachable message back to sender.The same period is represented as T2. Denoting the available bandwidth on the network path as BW,then we have the following formulas:T1=S1BW+S BW,T2=S3BW+S BWBW=S1−S312From the last formula,by measuring the difference betweenthe response time of P1,P2and P3,P4,the available networkbandwidth can be estimated.For each connection,the probingwill be repeated at adjustable intervals.The delay time can be measured using ping by sending outcertain ICMP Echo Request packets.After the measurementfor the network bandwidth and delay is done,the bandwidthdelay product can be calculated andthis value can be set asthe optimal socket buffer size for that connection.C.Structure of Tuning DaemonThe socket buffer tuning daemon consists of two compo-nents:the auto-tuning daemon,and the communicator(messagelistener).Both of them fork from the daemon startup processand share a set of common functional modules.Fig.1showsthe start up stage of our daemon.Fig.1.The Startup StageThe next two subsections will describe the routines of thetwo components.1)Auto-tuning Daemon:The auto-tuning daemon is incharge of monitoring the currently established TCP connec-tions in the local host,detecting the optimal socket buffer sizefor each speciﬁc connection and calling the modiﬁed systemcall to apply the changes.This procedure is repeated aftercertain delay period at the end of each round of tuning.Thereis also an interval between the process of probing and tuningeach connection.These delays and intervals are introduced toreduce the usage of TCP time and bandwidth,in order not tocause probingﬂood.Fig.2describes the steps.In each round of tuning,the auto-tuning daemon will callthe connection monitor to read the/proc/net/tcpﬁle forTCP connection status,include the socket pair informationfor each connection and the inode number related to that localsocket.After that the auto-tuning daemon calls the BandwidthDelay Product(BDP)detector to estimate the current availablebandwidth to the remote host of each connection,by using thealgorithm described in the previous section.After the BDP detection,the socket buffer for that connec-tion is changed to the optimal value.This is done by callinga new system call-setisockbuf.This system call searchesfor the corresponding socket structure in the kernel spaceaccordingto the inode number.Once found,it will set the receive socket buffer and send socket buffer size to the optimal value speciﬁed.During each round of tuning,the optimal socket buffer size for each remote host is cached in a dataﬁle.Each line of the dataﬁle contains information on one remote host.An example is given in Table II.The-1optimal value means theRepeat after delayFig.2.Flowchart of Auto-tuning Daemonprobing has failed because of time out or insufﬁcient number of probing packets received.The Remote IPﬁelds have been replaced with dummy IP addresses for security reasons.TABLE IIP ROBING C ACHERemote IP Optimal Value Timestamp10.0.0.17008010283327011234710.0.0.2-1102832289146985710.0.0.3297840102826959450087410.0.0.4-1102832251595988410.0.0.514600103959805131936The purpose of the probing cache is three-fold.First,if there are multiple TCP connections from the same remote host,the auto-tuning daemon will directly use the last estimated value for the optimal socket buffer size.Secondly,since the time stamp is also attached to each record,one can always deﬁne a timeout threshold for each line of record and after that line of cache expires,the auto-tuning daemon will try to probe that remote host again,given that the connection persists.Thirdly, the tuning daemon can compare the newly estimated value with the old one.In case the difference between the two values is larger than30%of the origianl optimal value,it is likely that the network condition may have changed.Then the auto-tuning daemon will send a tuning request message to the remote host. The format of the tuning request message is shown in Table III:TABLE IIIF ORMAT OF T UNING R EQUEST/R EPLY M ESSAGEmsg local local rem rem my your myheader ip port ip port optimal optimalﬁnalvalue value value msg header is a tag specifying the message type,using 1111to represent a tuning request and2222to represent a tuning reply.Theﬁelds following the msg header are local ip, local port,rem ip,and rem port.The names of theseﬁelds are self-explanatory.my optimal value refers to locally estimated optimal value for the socket buffer size.your optimal value refers to the optimal socket buffer size declared by the remote host of that connection.myﬁnal valueﬁeld is used when the tuning daemon receives a tuning request.The communicator will check the my optimal value in the incoming message and perform local estimation.The average value of the advertised optimal value and the locally estimated optimal value will be taken as theﬁnal value for the optimal socket buffer size.After that,the communicator will send back a tuning reply message containing thisﬁnal value.After tuning a connection,the daemon waits for two seconds to avoid side effects from the previous probe,before tuning the next.Between each round of probing,the daemon will wait another60-300seconds.After that wait period,the auto-tuning daemon repeats the same tuning procedure again.2)Communicator:The communicator mainly acts as a message listener.Its responsibility is to capture the tuning messages and respond to the message accordingly.The running procedure is shown in Fig.3.It listens to a preset port,which is 12345in current experiments.The communicator will handle any tuning message sent to this port from the remote host. The message format is as shown in Table III.First the message header will bechecked.Otherﬁelds will be parsed into related variables such as host IP address,port number,and optimal values.Fig.3.Flowchart of the CommunicatorIf the message is a tuning request,the communicator will call the BDP detection function to compute the optimal socket buffer size for that connection,just like what isdone by the auto-tuning daemon.After that,the communicator will take the average value of the locally tested optimal socket buffer and the remotely tested optimal buffer size and call the setisockbuf system call to change the socket buffer size.Following that,it will construct its own tuning reply message in a similar format,send it back as an acknowledge and close the connection.If the message is a tuning reply ,the communicator will print out the message and quit,since no further action is needed for the reply message.IV.E XPERIMENT R ESULTSThe tuning daemon and kernel patch was developed undera Pentium RIII 866MHz PC,running Red Hat Linux 7.2with kernel version 2.4.7-10.GCC 2.96and GNU make were used for compilation.The packet capture library libpcap [13]is used to monitor the throughput.The graphs were plotted by gnuplot .The TCP socket buffer sizes for the Linux kernel 2.4.7-10is shown in Table IV:TABLE IVF EATURES OF TCP IN L INUX KERNEL 2.4.7-10Linux Kernel Version2.4.7-10Max TCP socket buffer size 128KBytes Default TCP receive buffer size 87380Bytes Default TCP send buffer size16384BytesThe experiments were conducted from machine located in Center for Internet Research,NUS,Singapore to machine in Carnegie Mellon University,U.S.and another machine in Tokyo,provided by Asia-Paciﬁc Advanced Network Consor-tium(APAN).The topology of the testbed networks are shown in Fig.4.The maximum bandwidth available is limited to 100Mbits/s by the Ethernet card.Fig.4.Experiment Network TopologyThe conﬁgurations of the machines in cira ,cmui and tokxp are given in Table V:TABLE VC ONFIGURATIONS OF THE M ACHINES cira cmui tokxp Processor Pentium III Pentium II Pentium III 866MHz 400MHz 750MHz Memory 128M 512M 128M Operating RedHat LinuxRedHat LinuxRedHat LinuxSystem 7.27.17.3Network 100Mb/s 100Mb/s100Mb/s ConnectionEthernetEthernetEthernetThe ping program was used to measure the round trip time and pipechar to measure the available bandwidth.The roundtrip time from the local machine cira to remote machine cmui was 259.9ms,and the available bandwidth measured was 31.441Mbits/second.The round trip time from cira to remote machine tokxp is 238.6ms,and the available bandwidth is 4.835Mbits/second.The BDP of the network path from cira to cmui is thus 1045.95KBytes,and that of the network path from cira to tokxp is 147.66KBytes.To fully utilize the available network bandwidth,the socket buffer size has to be at least as large as bandwidth delay product value.In our experiments,pipechar uses UDP packets to probe the network paths.The probing result represents the possible throughput of UDP trafﬁc;however,tuning socket buffer alone may not be sufﬁcient for TCP trafﬁc achieve that throughput,due to the congestion control mechanism.Iperf [15]was used to visualize the relation between the socket buffer size and the throughput gained.Fig.5shows that as socket buffer size is increased from a small default value to the optimal value (bandwidth delay product),the throughput of the connection increases.After that optimal value has been reached,the throughput does not increase signiﬁcantly,even if socket buffer size increases.For cmui host,the maximum socket buffer size allowed is 128KBytes.For cira and tokxp the maximum socket buffer size allowed is set to 8MBytes.50100150200250 3003501 101001000 10000T h r o u g h p u t (K B y t e s /s )Socket Buffer Size (KBytes)Socket Buffer vs Throughputcmui tokxpFig.5.Relation between Socket Buffer and ThroughputThe ftp application was used to test our tuning daemon.According to the trafﬁc ﬂow direction,we call the data ﬂow from cira to cmui or tokxp upload and download if it is reverse.The size of the data ﬁle transferred between cira and cmui is 50MBytes.The size of the data ﬁle transferred between cira and tokxp is 20MBytes.To record the throughput of the ftp program,a monitor program was written to compute the instantaneous and average throughput.This monitor outputs the data into a log ﬁle and then gnuplot was used to plot out the ﬁnal result.To run the tuning daemon,root privilege is needed to patch the kernel and to start the tuning program.As root access is only available at cira and tokxp ,we can only run the tuning daemon and test the communication ability from these hosts.The cmui host can only be used for the one side tuning experiments.The experiments are described below:A.Experiments on cmuiFor connections using cmui host,only local side (cira )tuning is enabled.Given the round trip time is around 259ms and available bandwidth estimated is 31.4Mbits/second.The bandwidth delay product is 1045KBytes.1)Download:In this experiment,the sender side used the default send socket buffer.The receiver side has the tuning daemon running;thus,the receive socket buffer size is the estimated bandwidth delay product.50100150200 25030050100150200250S p e e d (K B y t e s /s e c )Time (second)Cmui - Download Average Speed GraphUntunedOne-side-TunedFig.6.Download from cmui to ciraAs shown in Fig.6,there is a large improvement of about 50KBytes/s for the throughput.The throughput of untuned data ﬂow varies around 240KBytes/s and the tuned data ﬂow achieves a throughput of 290KBytes/s.It shows that the default receive socket buffer of 87380bytes (about 85KBytes)is too insufﬁcient to fully utilize the network path.2)Upload:For the upload experiment,the local machine cira becomes the sender,and cmui is the receiver.Send socket buffer size was tuned to be the bandwidth delay product and receive socket buffer size was set to the default value of 85KBytes.For sender side tuning,the improvement in throughput is much less signiﬁcant than the receiver side tuning.The tuning daemon increased the send socket buffer size to be the optimal value 1045KBytes.The default send socket buffer size is 128KBytes and the default receive socket buffer is 85KBytes.The default receive socket buffer is smaller than both the untuned and tuned send socket buffer,which means that the bottleneck factor for the upload transmission can be either the receiver or network congestion and tuning the sender side may not affect the throughput effectively.As veriﬁed by Fig.7,the untuned data ﬂow attained a transmission throughput of 243KBytes and the tuned data ﬂow attained a throughput of 244KBytes.B.Experiments on tokxpFor host tokxp ,tuning daemon can be enabled on both side.Hence,three sets of tests were done,no tuning,tuning local501001502002500 50 100150 200 250S p e e d (K B y t e s /s e c )Time (second)Cmui - Upload Average Speed GraphUntunedOne-side-TunedFig.7.Upload from cira to cmuihost,and tuning both local and remote hosts.The round trip time from cira to tokxp is 238.6ms and the available band-width is 4.835Mbits/second.The bandwidth delay product is 147KBytes.1)Download:In the download test from tokxp ,we transfer the same amount of data three times,without tuning,with local side tuning and both local and remote tuning.20 40 60 80 100 120 140 160 180200 0 20 40 60 80100 120 140 160 180 200S p e e d (K B y t e s /s e c )Time (second)Tokxp - Download Average Speed GraphUntunedOne-side-Tuned Both-side-TunedFig.8.Download from tokxp to ciraIn Fig.8,the three data ﬂows experienced 3different trans-mission throughputs during the transmission of 20MBytes data ﬁle.The data ﬂow without tuning experienced the mini-mum throughput of 117.0KBytes/s;the data ﬂow with local side tuning enabled got a throughput of 157.9KBytes/s;and the data ﬂow with both side tuning enabled has got 190.5KBytes/s.The one-side tuning became the receiver-side tuning,since the data ﬂows from tokxp to cira .The data ﬂow withone side tuning enabled could achieve signiﬁcant throughput improvement over the data ﬂow without tuning.This is because the receiver side was the bottleneck;it could not advertise enough large window for the sender host to fully utilize the network.Once the receiver side is tuned,thethroughput is improved by 35%.When the tuning daemons were enabled on both sender and receiver sides,the data ﬂow with both side tuning enabled attained even higher throughput of 190.5KBytes.That is around 63%improvement over the untuned data ﬂow.In the ﬁrst 40seconds,the trafﬁc without tuning gets a better performance than the trafﬁc with one side tuning,due to the unstable connection condition.If we take the average over a few observations,the slight decrease is found non-typical.2)Upload:In the upload experiments,we did three tests in each round:one test without tuning,one test with local side tuning and one side with both side tuning.1020304050 60700 50 100 150200 250 300 350 400 450S p e e d (K B y t e s /s e c )Time (second)Tokxp - Upload Average Speed GraphUntunedOne-side-Tuned Both-side-TunedFig.9.Upload from cira to tokxpFig.9shows that the data ﬂow with both side tuning enabled obtained the best throughput.The data ﬂow with local side tuning has a throughput of 61.7KBytes/sec,slightly lower than both side tuning data ﬂow’s 66.9KBytes/s,but higher than that of the totally untuned data ﬂow,which is 51.1KBytes/s.In this upload experiment,the network is more congested than in the download experiment;so even the tuning daemon can enlarge the socket buffer accordingly,the throughput is still limited by the network condition.From the above experiment results,we ﬁnd that for the tuning daemon to achieve good effects,not only should the network paths have a long delay and high bandwidth,but the network congestion should also be low.If the available bandwidth is limited to a small value by congestion,the default socket buffer size should be sufﬁcient.The overhead of our tuning daemon will instead adversely affect the performance.On low congestion,long delay and high bandwidth network path,enabling the tuning daemon on both ending hosts will improve the throughput more than enabling the tuning daemon on one side.V.C ONCLUSION AND FUTURE WORKThe TCP socket buffer auto-tuning daemon has been shown to be effective in improving network throughput under the same network condition and host load.It is useful especiallyfor high bandwidth and long delay network paths.No ad-ditional application layer changes need to be made for the user level programs to beneﬁt from the effects of the tuning daemon.The program is extensible as the developer can add other modules into the daemon program to estimate bandwidth delay product or derive a more suitable socket buffer size from that.Finally,although the tuning daemon was developed under Linux,it is portable to other UNIX like systems.For future development of the tuning daemon,we plan to do the following:The daemon will be changed to be event-driven and tune the system according to the real time network performance changes.More network related parameters can be included for tuning besides the socket buffer sizes.More efﬁcient and accurate algorithms are necessary to determine the optimal values for the socket buffer stly,both active and passive measurements will be needed for the tuning daemon,in order to reduce redundant probing.A CKNOWLEDGMENTWe would like to thank Nancy and Hua Chu of CMU,and Kitamura-san and Kitatsuji-san of APAN Tokyo for providing us the support for experimental network setup.We would also like to thank Srijith and Saravanan for their time and effort spent in our experiments and reﬁnement of this paper.This work was supported by A*STAR Singapore under the BB21grant.R EFERENCES[1]W.Richard Stevens,TCP/IP Illustrated Volume 1:The Protocols ,Addison-Wesley,1994.[2]J.Mogul,S.Deering,RFC 1191:Path MTU Discovery ,November 1990.[3]V .Jacobson,R.Braden,and D.Borman,RFC 1323:TCP Extensions forHigh Performance ,May 1992.[4]M.Mathis,J.Mahdavi,S.Floyd and A.Romanow,RFC 2018:TCPSelective Acknowledgement Options ,October 1996.[5]Brian L.Tierney,TCP Tuning Guide for Distributed Application onWide Area Networks(2000).Data Intensive Distributed Computing Group,Lawrence Berkeley National Laboratory.[6]Web100:Facilitating Effective and transparent Network Use ,/,2002.[7]J.Semke,J.Mahdavi,and M.Mathis,Automatic TCP Buffer Tuning ,inproceedings of ACM SIGCOMM’98pp.315-323,August 1998.[8]Gaurav Navlakha,Jim Ferguson,Auto Tuning Enabled FTP Client andServer v2.0,/Projects/Autobuf/,2001[9]Srinivasan Keshav,a Control-Theoretic Approach to Flow Control ,InProceedings of ACM SIGCOMM,1991.[10]Brian L.Tierney,Dan Gunter,Jason Lee,Martin Soufer,EnablingNetwork-Aware Applications ,Computing Sciences Directorate,Lawrence Berkeley National Laboratory,University of California,Berkeley,CA,94720.[11] E.Weigle,Wuchun Feng,A comparison of TCP Automatic TuningTechniques for Distributed Computing ,High Performance Distributed Computing,2002.HPDC-112002.Proceedings.11th IEEE International Symposium on,2002,Page(s):265-272.[12]Jin Guojun,etc al,Network Characterization Service (NCS),/pipechar/,2002.[13]Van Jacobson,Craig Lores and Steven McCanne,pcap -Packet Capturelibrary ,/,2002.[14]M.Beck,H.Boheme,etc al,LinuxKernel Internals (2nd ed),Addison-Wesley,1997.[15]Ajay Tirumala,Feng Qin,Jon Dugan,Jim Ferguson,and KevinGibbs,Iperf -The TCP/UDP Bandwidth Measurement Tool ,/Projects/Iperf/,2003.[16]M.Allman,V .Paxson,W.Stevens,RFC 2581:TCP Congestion Control ,1999.毕业设计（论文）附件外文文献翻译学号：姓名：所在系别：专业班级：指导教师：原文标题：A TCP Socket Buffer Auto-tuning Daemon年月日套接字缓冲区的TCP自动调谐守护进程1摘要根据不同的带宽和网络路径的往返时间，确定的网络连接可能需要不同的套接字缓冲区比别人。

英国布拉德福德大学

英国布拉德福德大学University of Bradford/学校简介：英国布拉德福德大学(University of Bradford)是一所具有130多年悠久历史的英国老牌综合性公立大学，它的前身为布拉德福德纺织、设计及建筑学院。

该校于1882年更名为布拉德福德技术学院，再于1957年更名为布拉德福德理工大学。

学校于1966年得到皇家认证并正式改称布拉德福德大学。

布拉德福德大学1966年并入附近的布拉德福德及阿尔代尔卫生学院，使之成为布拉德福德大学现在的卫生学院。

1980年代学校为了给和平学习系更多的空间而关闭了物理系。

被称作和平学习的这个专业是布拉德福德大学目前所有专业中最出色的专业之一，该系和联合国有着密切的联系。

布拉德福德大学校园内有一流的教学设施以及配套的图书馆、计算机中心、书店、体育中心、洗衣房、商店、酒吧、餐厅、电影院及娱乐场所。

其管理类硕士(MA)在全英排名第二位，工商管理硕士(MBA)在全英排名第十位，世界排名第七十六位，但按照性价比布拉德福德大学的MBA世界排名第四位。

管理类本科课程全英排名第六位。

学校优势：布拉德福德大学拥有10000多名学生，其中约有22%是来自世界110多个国家和地区的国际留学生，毕业生的就业率在约克郡名列第一。

根据泰晤士报在2005年进行的的调查，布拉德福德大学是2005年全英就业率第二高的大学，仅次于剑桥大学。

布拉德福德大学的很多专业的学生在毕业后6个月就可以就业。

布拉德福德大学拥有10000多名学生，其中约有22%是来自世界110多个国家和地区的国际留学生,毕业生的就业率在约克郡名列第一。

根据泰晤士报在2005年进行的的调查，布拉德福德大学是2005年全英就业率第二高的大学，仅次于剑桥大学。

●布拉德福德大学环境优美、建筑古老、教学设备先进、教学水平世界闻名。

深度强化学习模型轻量化算法研究

Computer Science and Application 计算机科学与应用, 2023, 13(4), 779-788 Published Online April 2023 in Hans. https:///journal/csa https:///10.12677/csa.2023.134077深度强化学习模型轻量化算法研究安天一，李宁，王超北京信息科技大学计算机学院，北京收稿日期：2023年3月18日；录用日期：2023年4月17日；发布日期：2023年4月23日摘要针对深度强化学习网络难以部署到资源受限终端设备的问题，本文提出一种深度神经网络优化压缩算法。

该算法引入倒残差模块作为主干网络，实现网络的轻量化；采用基于响应的知识蒸馏，以动作策略为蒸馏目标，弥补网络轻量化造成的精度损失；采用基于特征的知识蒸馏，对网络中间层的特征向量进行蒸馏，进一步提升网络精度。

实验结果表明，轻量化后的网络参数量为19.79M ，参数量为原网络的59.8%，性能提升约12.1%，且在网络轻量化的同时，提升了模型表现，验证了所提算法的有效性。

关键词深度强化学习，轻量化设计，知识蒸馏Research on Lightweight Algorithms for Deep Reinforcement LearningTianyi An, Ning Li, Chao WangSchool of Computing, Beijing Information Science and Technology University, BeijingReceived: Mar. 18th , 2023; accepted: Apr. 17th , 2023; published: Apr. 23rd, 2023AbstractIn response to the difficulty of deploying deep reinforcement learning networks on resource- con-strained terminal devices, a deep neural network optimization compression algorithm is proposed in this paper. This algorithm introduces an inverse residual module as the backbone network to achieve the lightweight of network; adopts response-based knowledge distillation, with action strate-gy as the distillation target, to make up for the accuracy loss caused by the lightweight of network; adopts feature-based knowledge distillation to distill the feature vectors in the middle layer of the network, further improving network accuracy. Experimental results show that the parameter size of the lightweight network is 19.79M, the parameter size is 59.8% of the original network, the per-安天一等formance is improved by about 12.1%, and the model performance is improved while the network is lightweight, verifying the effectiveness of the proposed algorithm.KeywordsDeep Reinforcement Learning, Lightweight Design, Knowledge Distillation Array Copyright © 2023 by author(s) and Hans Publishers Inc.This work is licensed under the Creative Commons Attribution International License (CC BY 4.0)./licenses/by/4.0/1. 引言随着深度学习(Deep Learning, DL)技术的不断发展，其凭借深度神经网络强大的特征表达能力，为学术界和工业界解决了许多的难题并取得了众多令人瞩目的研究成果。

001-Searchable Encryption(SE)加密搜索

XI’AN TECHNOLOGICAL UNIVERSITY
To encrypt, one encryption, one XOR, and two pseudo-random functions have to be computed per word per document. The search requires one XOR and one pseudo-random functions per word per document. Security: The scheme uses no formal security definition. After several queries, it is possible to learn the words inside the documents with statistical anal学知行相长
General model for SE
XI’AN TECHNOLOGICAL UNIVERSITY
精工博艺忠诚进取
敦德励学知行相长
SE architectures
• single writer/single reader (S/S)
In S/S scheme, the secret key owner is allowed to create searchable content and to generate trapdoors to search.
精工博艺忠诚进取敦德励学知行相长
M/S scheme (Cont.)
Efficiency: The encryption requires the server to perform one pairing computation, two exponentiations, and two hashes per keyword.

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• Hongjun Su, Assistant Professor
January 2003 4
Introduction
School of Computing, Armstrong Atlantic State University
January 2003 5
Introduction
School of Computing, Armstrong Atlantic State University
• Mark Burge, Assistant Professor
Doctor of Technical Sciences, Johannes Kepler University, 1998 Biometrics, Computer Vision, Image Processing, Pattern Recognition
Outline
• • • • • • • Introduction Programs School of Computing Growth Enrollment Grants Yamacraw Community Outreach
School of Computing, Armstrong Atlantic State University
January 2003 3
Outline
• • • • • • • Introduction Programs School of Computing Growth Enrollment Grants Yamacraw Community Outreach
School of Computing, Armstrong Atlantic State University
January 2003 9
Department of Computer Science
• • • • • • 10 Faculty About 300 Majors About 20 Graduate Students Bachelor of Science Master of Science in Computer Science Accredited by CAC (The Computing Accreditation Commission) of ABET (The Accrediting Board for Engineering and Technology)
Department of Computer Science
• Y. Daniel Liang, Professor
Ph.D., University of Oklahoma, 1991 Database Systems, Graph Algorithms, Programming Languages, Software Engineering
January 2003 10
School of Computing, Armstrong Atlantic State University
Department of Computer Science
Faculty • Geir Agnarrson, Assistant Professor
Ph.D., University of California at Berkeley, 1996 Algorithms, Combinatorics, and Graph Theory
January 2003 6
Introduction
School of Computing, Armstrong Atlantic State University
January 2003 7
Introduction
School of Computing, Armstrong Atlantic State University
January 2003 8
Outline
• • • • • • • Introduction Programs School of Computing Growth Enrollment Grants Yamacraw Community Outreach
School of Computing, Armstrong Atlantic State University
• Ray Hashemi, Professor • Steve Jodis, Assistant Dean and Associate Professor
Ph.D., Auburn University, 1994 Software Engineering, Software Metrics
School of Computing, Armstrong Atlantic State University January 2003 11
School of Computing Armstrong Atlantic State University
January 2003
Ray Greenlaw
School of Computing, Armstrong Atlantic State University January 2003 1
• Sergio DeAgostino, Assistant Professor
Ph.D., University of Rome “La Sapienza,” 1992 Algorithms, Content Processing, Data Compression Ph.D., University of Missouri at Columbia, 1983 Data Mining
• Joy Reed, Professor
Ph.D., Auburn University, 1972 Distributed Systems
• Charles Shipley, Professor
Ph.D., University of Nebraska, 1972 Architecture, Operating Systems, Software Tools