网格计算和云计算 360 度比较原文.docx

1、网格计算和云计算 360 度比较原文Cloud Computing and Grid Computing 360-Degree Compared1,2,3Ian Foster, 4Yong Zhao, 1Ioan Raicu, 5Shiyong Lufostermcs.anl.gov, yozha, iraicucs.uchicago.edu, shiyongwayne.edu1 Department of Computer Science, University of Chicago, Chicago, IL, USA2Computation Institute, University of

2、 Chicago, Chicago, IL, USA3Math & Computer Science Division, Argonne National Laboratory, Argonne, IL, USA4Microsoft Corporation, Redmond, WA, USA5 Department of Computer Science, Wayne State University, Detroit, MI, USAAbstract Cloud Computing has become another buzzword after Web2.0. However, ther

3、e are dozens of different definitions for Cloud Computing and there seems to be no consensus on what a Cloud is. On the other hand, Cloud Computing is not a completely new concept; it has intricate connection to the relatively new but thirteen-year established Grid Computing paradigm, and other rele

4、vant technologies such as utility computing, cluster computing, and distributed systems in general. This paper strives to compare and contrast Cloud Computing with Grid Computing from various angles and give insights into the essential characteristics of both.1 100-Mile OverviewCloud Computing is hi

5、nting at a future in which we wont compute on local computers, but on centralized facilities operated by third-party compute and storage utilities. We sure wont miss the shrink-wrapped software to unwrap and install. Needless to say, this is not a new idea. In fact, back in 1961, computing pioneer J

6、ohn McCarthy predicted that “computation may someday be organized as a public utility” and went on to speculate how this might occur.In the mid 1990s, the term Grid was coined to describe technologies that would allow consumers to obtain computing power on demand. Ian Foster and others posited that

7、by standardizing the protocols used to request computing power, we could spur the creation of a Computing Grid, analogous in form and utility to the electric power grid. Researchers subsequently developed these ideas in many exciting ways, producing for example large-scale federated systems (TeraGri

8、d, Open Science Grid, caBIG, EGEE, Earth System Grid) that provide not just computing power, but also data and software, on demand. Standards organizations (e.g., OGF, OASIS) defined relevant standards. More prosaically, the term was also co-opted by industry as a marketing term for clusters. But no

9、 viable commercial Grid Computing providers emerged, at least not until recently.So is “Cloud Computing” just a new name for Grid? In information technology, where technology scales by an order of magnitude, and in the process reinvents itself, every five years, there is no straightforward answer to

10、 such questions.Yes: the vision is the sameto reduce the cost of computing, increase reliability, and increase flexibility by transforming computers from something that we buy and operate ourselves to something that is operated by a third party.But no: things are different now than they were 10 year

11、s ago. We have a new need to analyze massive data, thus motivating greatly increased demand for computing. Having realized the benefits of moving from mainframes to commodity clusters,we find that those clusters are quite expensive to operate. We have low-cost virtualization. And, above all, we have

12、 multiple billions of dollars being spent by the likes of Amazon, Google, and Microsoft to create real commercial large-scale systems containing hundreds of thousands of computers. The prospect of needing only a credit card to get on-demand access to 100,000+ computers in tens of data centers distri

13、buted throughout the worldresources that be applied to problems with massive, potentially distributed data, is exciting! So we are operating at a different scale, and operating at these new, more massive scales can demand fundamentally different approaches to tackling problems. It also enablesindeed

14、 is often only applicable toentirely new problems.Nevertheless, yes: the problems are mostly the same in Clouds and Grids. There is a common need to be able to manage large facilities; to define methods by which consumers discover, request, and use resources provided by the central facilities; and t

15、o implement the often highly parallel computations that execute on those resources. Details differ, but the two communities are struggling with many of the same issues.1.1 Defining Cloud ComputingThere is little consensus on how to define the Cloud 49. We add yet another definition to the already sa

16、turated list of definitions for Cloud Computing:A large-scale distributed computing paradigm that is driven by economies of scale, in which a pool of abstracted, virtualized, dynamically-scalable, managed computing power, storage, platforms, and services are delivered on demand to external customers

17、 over the Internet.There are a few key points in this definition. First, Cloud Computing is a specialized distributed computing paradigm; it differs from traditional ones in that 1) it is massively scalable,2) can be encapsulated as an abstract entity that delivers different levels of services to cu

18、stomers outside the Cloud, 3) itis driven by economies of scale 44, and 4) the services can be dynamically configured (via virtualization or other approaches) and delivered on demand.Governments, research institutes, and industry leaders are rushing to adopt Cloud Computing to solve their ever- incr

19、easing computing and storage problems arising in the Internet Age. There are three main factors contributing to the surge and interests in Cloud Computing: 1) rapid decrease in hardware cost and increase in computing power and storage capacity, and the advent of multi-core architecture and modern su

20、percomputers consisting of hundreds of thousands of cores;2) the exponentially growing data size in scientific instrumentation/simulation and Internet publishing and archiving; and 3) the wide-spread adoption of Services Computing and Web 2.0 applications.1.2 Clouds, Grids, and Distributed SystemsMa

21、ny discerning readers will immediately notice that our definition of Cloud Computing overlaps with many existing technologies, such as Grid Computing, Utility Computing, Services Computing, and distributed computing in general. We argue that Cloud Computing not only overlaps with Grid Computing, it

22、is indeed evolved out of Grid Computing and relies on Grid Computing as its backbone and infrastructure support. The evolution has been a result of a shift in focus from an infrastructure that delivers storage and compute resources (such is the case in Grids) to one that is economy based aiming to d

23、eliver more abstract resources and services (such is the case in Clouds). As for Utility Computing, it is not a new paradigm of computing infrastructure; rather, it is a business model in which computing resources, such as computation and storage, are packaged as metered services similar to a physic

24、al public utility, such as electricity and public switched telephone network. Utility computing is typically implemented using other computing infrastructure(e.g. Grids) with additional accounting and monitoring services. A Cloud infrastructure can be utilized internally by a company or exposed to t

25、he public as utility computing.See Figure 1 for an overview of the relationship between Clouds and other domains that it overlaps with. Web 2.0 covers almost the whole spectrum of service-oriented applications, where Cloud Computing lies at the large-scale side. Supercomputing and Cluster Computing

26、have been more focused on traditional non-service applications. Grid Computing overlaps with all these fields where it is generally considered of lesser scale than supercomputers and Clouds.Figure 1: Grids and Clouds OverviewGrid Computing aims to “enable resource sharing and coordinated problem sol

27、ving in dynamic, multi-institutional virtual organizations” 1820. There are also a few key features to this definition: First of all, Grids provide adistributed computing paradigm or infrastructure that spans across multiple virtual organizations (VO) where each VO can consist of either physically d

28、istributed institutions or logically related projects/groups. The goal of such a paradigm is to enable federated resource sharing in dynamic, distributed environments. The approach taken by the de facto standard implementation The Globus Toolkit 1623, is to build a uniform computing environment from

29、 diverse resources by defining standard network protocols and providing middleware to mediate access to a wide range of heterogeneous resources. Globus addresses various issues such as security, resource discovery, resource provisioning and management, job scheduling, monitoring, and data management

30、.Half a decade ago, Ian Foster gave a three point checklist 19 to help define what is, and what is not a Grid: 1) coordinates resources that are not subject to centralized control, 2) uses standard, open, general-purpose protocols and interfaces, and 3) delivers non-trivial qualities of service. Alt

31、hough point 3 holds true for Cloud Computing, neither point 1 nor point 2 is clear that it is the case for todays Clouds. The vision for Clouds and Grids are similar, details and technologies used may differ, but the two communities are struggling with many of the same issues. This paper strives to

32、compare and contrast Cloud Computing with Grid Computing from various angles and give insights into the essential characteristics of both, with the hope to paint a less cloudy picture of what Clouds are, what kind of applications can Clouds expect to support, and what challenges Clouds are likely to face in the coming years as they gain momentum and adoption. We hope this will help both communities gain deeper understanding of the goals, assumptions, status, and directions, and provide a more detailed view of both