计算机专业外文翻译.docx

1、计算机专业外文翻译A Tale of Clouds: Paradigm Comparisons and Some Thoughts on Research Issues*Lijun MeiThe University of Hong KongPokfulam, Hong Kongljmeics.hku.hkCloud computing is an emerging computing paradigm. It aims to share data, calculations, and services transparentlyamong users of a massive grid. A

2、lthough theindustry has started selling cloud-computing products, research challenges in various areas, such as UI design, task decomposition, task distribution, and task coordination, are still unclear. Therefore, we study the methods toreason and model cloud computing as a step towardidentifying f

3、undamental research questions in this paradigm.In this paper, we compare cloud computing withservice computing and pervasive computing. Both theindustry and research community have actively examined these three computing paradigms. We draw a qualitative comparison among them based on the classic mod

4、el ofcomputer architecture. We finally evaluate the comparisonresults and draw up a series of research questions incloud computing for future exploration.Keywords: cloud computing, paradigm comparison.1. IntroductionCloud computing is a paradigm that focuses on sharingdata and computations over a sc

5、alable network of nodes.Examples of such nodes include end user computers, datacenters, and Web Services. We term such a network ofnodes as a cloud. An application based on such clouds istaken as a cloud application.This paradigm is increasingly popular in the industry,where industrial leaders such

6、as Microsoft 26, Google2, and IBM 5 strongly promote the paradigm in recentyears. An early attempt to formulate cloud computingdates back to at least 1997 8. However, to our bestknowledge, the adoption and promotion of cloudcomputing has been slow until 2007 9.We observe that the history of early in

7、dustrialadoptions of cloud computing share some commonmilestones with that of service computing 4. Forexample, it took service computing 27 a long time (tenyears or so) to receive worldwide support from leadingcompanies like IBM, Microsoft 25, BEA, and Oracle.Similarly, it has been many years since

8、the earlyformalization effort 8 toward cloud computing.Besides, the wide adoption of a computing paradigmusually depends highly on the maturity of supportingtechnologies and industry recognitions. Service computinghas become much more popular since the success ofWeb services, although a Web service

9、is only one of thetechnologies to fulfill the notion of service orientation 4.Similarly, the distributed computing community haspointed out that many distributed computing techniquesfor cloud computing have been mature 71011. Manycompanies such as Dell and IBM have begun to shipcloud computing machi

10、nes 510.Last but not the least, in either service computing orcloud computing, research developments lag behindindustrial adoptions. For instance, COSCON, a leadinginternational container shipper, has a successful adoptionof service computing. It successfully used service-orientedarchitecture to imp

11、rove the business responsibility tocustomers in 2004 3. Yet, research studies in serviceorientedarchitecture from the software engineeringcommunity 19 are still inadequate.Despite our survey over the Internet, to our bestknowledge, there are few articles to pinpoint research* 2008 IEEE. This materia

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14、s, or to reuse any copyrighted component of this work inother works must be obtained from the IEEE. This research is supported in part by the General Research Fund of theResearch Grant Council of Hong Kong (project nos. 111107, 717308,and 717506). Corresponding author.2issues in cloud computing. Thi

15、s would slow down thenext research advances. We will alleviate this problem inthe present paper.In this paper, we use the classic computer architecturemodel 15 to provide a qualitative comparison frameworkto compare cloud computing with pervasive computingand service computing. The qualitative compa

16、risonframework includes three features: input-output (I/O),storage, and calculation. For each feature, we draw thecomparison using multiple characteristics. Through suchcomparisons, we identify the connections between cloudcomputing and the other two computing paradigms fromthe perspective of softwa

17、re engineering. Based on theconnections, we draw up a few research issues and discussthem in the paper to promote future exploration.The main contribution of the paper is twofold: (i) Toour best knowledge, we provide the first qualitativecomparison on cloud computing, service computing, andpervasive

18、 computing. (ii) We present a series of researchissues in cloud computing on top of the comparisonframework. These issues promote future explorations.The rest of the paper is organized as follows: Section 2presents the preliminaries of cloud computing, servicecomputing, and pervasive computing. Sect

19、ion 3 introducesour qualitative framework to compare the abovethree computing paradigms and present our efforts toidentify research issues in cloud computing. Finally, wereview related work in Section 4 and draw a conclusion inSection 5.2. PreliminariesThis section reviews the preliminaries of cloud

20、computing, service computing, and pervasive computing.2.1. Cloud computingAs we have introduced in Section 1, a computing cloudis a massive network of nodes. Thus, scalability should bea quality feature of the computing cloud. It has at leasttwo dimensions, namely horizontal cloud scalability andver

21、tical cloud scalability (adapted from 9).􀁺 Horizontal cloud scalability is the ability to connectand integrate multiple clouds to work as one logicalcloud. For instance, a cloud providing calculationservices (calculation cloud) can access a cloudproviding storage services (storage cloud) to

22、 keepintermediate results. Two calculation clouds can alsointegrate into a larger calculation cloud.􀁺 Vertical cloud scalability is the ability to improve thecapacity of a cloud by enhancing individual existingnodes in the cloud (such as providing a server withmore physical memory) or impro

23、ving the bandwidththat connects two nodes. In addition, to meet increasingmarket demand, a node can be gradually upgraded froma single power machine to a data center.Scalability should be transparent to users. For instance,users may store their data in the cloud without the need toknow where it keep

24、s the data or how it accesses the data.For simplicity, we will refer to horizontal and verticalcloud scalability, respectively, as horizontal scalabilityand vertical scalability in this paper.2.2. Service computingService computing (or service-oriented computing) isan emerging paradigm to model, cre

25、ate, operate, andmanage business services. In this paradigm, servicespublish themselves in public registries, discover peerservices, and bind to the latter services to form servicecompositions using standardized protocols 6. To create aservice composition, engineers may use a specification,such as W

26、S-BPEL 30, to model the collaborative needin workflows. To carry out individual workflow steps,software developers may use Web services, the mostpopular way to fulfill service-oriented architecture in theindustry. A set of service-oriented applications over theWeb services thus creates a network of

27、services.Service Service RegistryRegister Serviceto RegistryDiscover Servicefrom a registryBind Service Associate ServiceFigure 1. Service-oriented network 18.We briefly describe a service-oriented network 18 tofacilitate the comparison in the rest of the paper. Anelement in such a network is a serv

28、ice registry, serviceconsumer, or service provider. A service providerregisters itself in a service registry. A service consumerfirst discovers the service from a registry, and then bindsto the service. A service provider may register itself tomore than one registry. A registry may also associate it

29、sregistered services to other registries, and acts as a serviceitself. Such a treatment on a registry provides a genericview among elements in service-oriented modeling.2.3. Pervasive computingPervasive computing (or ubiquitous computing) 2324is another emerging computing paradigm. Software (oftenre

30、ferred as pervasive software) can be embedded in aconstantly changing computing environment. Therefore,pervasive software users do not need to be concernedabout how to adjust the software to adapt to thesurrounding computing environment. A well-developedenvironment will enable users to use pervasive

31、 softwareeverywhere without extra effort.To understand and react to a user, applications useenvironmental features, known as contexts, extensively.Sensors can capture these contexts. To allow ubiquitous3support to end users, smart sensors are placed aroundusers to preserve different information, such as thelocations, contexts, and user-relevant data.Figure 2 shows a pervasive computing example.Sensors, mobile phones and PDAs, desktop computer, andservers are interconnected logically to form an application.Suppose a nomadic user at the top left corner of Figure 2moves from using a lapt