Cloud Computing and Grid Computing 360-Degree Compared
1,2,3
Ian Foster,
4
Yong Zhao,
1
Ioan Raicu,
5
Shiyong Lu
foster@mcs.anl.gov, yozha@microsoft.com, iraicu@cs.uchicago.edu, shiyong@wayne.edu
1
Department of Computer Science, University of Chicago, Chicago, IL, USA
2
Computation Institute, University of Chicago, Chicago, IL, USA
3
Math & Computer Science Division, Argonne National Laboratory, Argonne, IL, USA
4
Microsoft Corporation, Redmond, WA, USA
5
Department of Computer Science, Wayne State University, Detroit, MI, USA
Abstract– Cloud Computing has become another buzzword after Web
2.0. However, there 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 relevant
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 Overview
Cloud Computing is hinting at a future in which we won’t
compute on local computers, but on centralized facilities
operated by third-party compute and storage utilities. We sure
won’t 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 John 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 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 (TeraGrid,
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
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 such questions.
Yes: the vision is the same—to 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 years 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 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 distributed
throughout the world—resources 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 enables—indeed is
often only applicable to—entirely 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 to 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 Computing
There is little consensus on how to define the Cloud [49]. We
add yet another definition to the already saturated 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 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 customers outside the Cloud, 3) it
is 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-
increasing 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
supercomputers consisting of hundreds of thousands of cores;
Authorized licensed use limited to: Universitat Wien. Downloaded on June 4, 2009 at 11:02 from IEEE Xplore. Restrictions apply.
剩余9页未读，继续阅读
资源评论
