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Grid computing
POWER OF SUPERCOMPUTER ON A DESKTOP
Imagine that every time you switch on a fan, you had to decide which power station should supply the electricity. Worse still, you could only select from those power stations that were built by the company that made the fan. If the power station chosen happened to be running at full capacity, fan will not work. Replace the fan with a personal computer and electrical power with computational power, and this gives a measure of frustration facing those who dream of distributing large computing problems to dozens, hundreds even millions of computers in the internet. If we take the analogy of a fan and electrical power then the logical conclusion would be personal computer and grid computing.
Grid computing is the aggregating of processing power possessed by different servers or workstations into a single resource. A single large job can be split into smaller pieces and run on several, or several thousand, computers simultaneously, producing supercomputer speed from off-the-shelf hardware. Grid Computing enables the virtualization of distributed computing resources such as processing, network bandwidth and storage capacity to create a single system image, granting users and applications seamless access to vast IT capabilities. Just as an Internet user views a unified instance of content via the Web, a Grid user essentially sees a single, large virtual computer. At its core, Grid Computing is based on an open set of standards and protocols (i.e., Open Grid Services Architecture: OGSA) that enable communication across heterogeneous, geographically dispersed IT environments.
Applications of Grid Computing
Many application domains in which large processing problems can easily be divided into subproblems and solved independently are already taking great advantage of grid computing. These include Monte Carlo simulations and parameter sweep applications, such as ionization chamber calibration, drug design, operations research, electronic CAD, and ecological modeling.
On other fronts, projects such as Distributed.net, launched in 1997, and SETI@home, launched in 1999, attracted worldwide attention to peer-to-peer computing (P2P). Millions of participants contributed their PCs' idle CPU cycles: for Distributed.net, they processed RSA Labs RC5-32/12/7 (56-bit) secret key challenge; participants in SETI@home processed a database of large pulsar signals in a search for extraterrestrial intelligence. Emerging from these successes are the notions of virtual organizations and virtual enterprises, which could develop a computational economy for sharing and aggregating resources to solve problems.
IBM is a strong supporter of Grid-based architectures and a founding partner of the Globus Project, a multi-institutional research and development effort. Founded by a team of technicians and researchers, the Globus Project has defined an open-source Grid reference architecture and a set of tools to assist in the implementation of Grids. IBM is not alone in devoting research resources to grid computing. Hewlett-Packard Co., Compaq Computer Corp., Intel Corp., and other computing industry heavyweights are also devoting an increasing amount of their research resources to grid computing. CANARIE, the CA*net3 Internet network and the National Research Council have formed Grid Canada to pursue grid computing projects.
Comparison of grid computing to clusters, the Web, P2P and distributed computing.
Clusters are homogeneous and in close physical proximity to one another, while Grids can be heterogeneous and geographically distributed. Clusters are examples of computing resources that could be included in a Grid.
Grid differs from the Web in that it empowers multiple resources to collaborate toward a common goal, while the Web primarily enables communication. The Web enabled unparalleled access to information via Web browsers/HTML. Grids enable access to diverse Internet resources, such as computers and data storage, for utilization by public and private organizations (e.g., government, businesses).
Peer-to-peer computing involves file sharing between two users, while Grid Computing shares resources among many.
Grid computing is the accomplishment of computational tasks on a set of computers connected by a network. This is similar to distributed computing, except with a more finely grained implementation for task assignment and coordination among the grid elements. Grid computing also has a security model built in, such that a desired level of security and access control may be implemented at all levels of the grid infrastructure.
Capabilities of grid computing
The easiest use of grid computing is to run an existing application on a different machine. The machine on which the application is normally run might be unusually busy due to an unusual peak in activity. The job in question could be run on an idle machine elsewhere on the grid. There are at least two prerequisites for this scenario. First, the application must be executable remotely and without undue overhead. Second, the remote machine must meet any special hardware, software, or resource requirements imposed by the application. For example, a batch job that spends a significant amount of time processing a set of input data to produce an output set is perhaps the most ideal and simple use for a grid. If the quantities of input and output are large, more thought and planning might be required to efficiently use the grid for such a job. It would usually not make sense to use a word processor remotely on a grid because there would probably be greater delays and more potential points of failure.
In most organizations, there are large amounts of underutilized computing resources. Most desktop machines are busy less than 5% of the time. In some organizations, even the server machines can often be relatively idle. Grid computing provides a framework for exploiting these underutilized resources and thus has the possibility of substantially increasing the efficiency of resource usage.
The processing resources are not the only ones that may be underutilized. Often, machines may have enormous unused disk drive capacity. Grid computing, more specifically, a "data grid", can be used to aggregate this unused storage into a much larger virtual data store, possibly configured to achieve improved performance and reliability over that of any single machine. If a batch job needs to read a large amount of data, this data could be automatically replicated at various strategic points in the grid. Thus, if the job must be executed on a remote machine in the grid, the data is already there and does not need to be moved to that remote point. This offers clear performance benefits. Also, such copies of data can be used as backups when the primary copies are damaged or unavailable.
Another function of the grid is to better balance resource utilization. An organization may have occasional unexpected peaks of activity that demand more resources. If the applications are grid enabled, they can be moved to underutilized machines during such peaks. In fact, some grid implementations can migrate partially completed jobs. In general, a grid can provide a consistent way to balance the loads on a wider federation of resources. This applies to CPU, storage, and many other kinds of resources that may be available on a grid. Management can use a grid to better view the usage patterns in the larger organization, permitting better planning when upgrading systems, increasing capacity, or retiring computing resources no longer needed.
The potential for massive parallel CPU capacity is one of the most attractive features of a grid. In addition to pure scientific needs, such computing power is driving a new evolution in industries such as the bio-medical field, financial modeling, oil exploration, motion picture animation, and many others. The common attribute among such uses is that the applications have been written to use algorithms that can be partitioned into independently running parts. A CPU intensive grid application can be thought of as many smaller "subjobs," each executing on a different machine in the grid. To the extent that these subjobs do not need to communicate with each other, the more "scalable" the application becomes. A perfectly scalable application will, for example, finish 10 times faster if it uses 10 times the number of processors. Barriers often exist to perfect scalability. The first barrier depends on the algorithms used for splitting the application among many CPUs. If the algorithm can only be split into a limited number of independently running parts, then that forms a scalability barrier. The second barrier appears if the parts are not completely independent; this can cause contention, which can limit scalability. For example, if all of the subjobs need to read and write from one common file or database, the access limits of that file or database will become the limiting factor in the application's scalability. Other sources of inter-job contention in a parallel grid application include message communications latencies among the jobs, network communication capacities, synchronization protocols, input-output bandwidth to devices and storage devices, and latencies interfering with real-time requirements.
What the grid cannot do?
A word of caution should be given to the overly enthusiastic. The grid is not a silver bullet that can take any application and run it a 1000 times faster without the need for buying any more machines or software. Not every application is suitable or enabled for running on a grid. Some kinds of applications simply cannot be parallelized. For others, it can take a large amount of work to modify them to achieve faster throughput. The configuration of a grid can greatly affect the performance, reliability, and security of an organization's computing infrastructure. For all of these reasons, it is important for us to understand how far the grid has evolved today and which features are coming tomorrow or in the distant future.
References:
1. www.ibm.com/grid/index.html
2. Computer networks computing power on tap, The Economist, 21 June 2001
3. In your future: computing power on demand, E-Commerce Times, 2 August 2001
4. Weaving Computational Grids: How analogous are they with electrical grids? Madhu chetty, monash university, and Rajkumar Buyya, university of Melbourne, Australia.
5.The Grid Report: The commercial implications of the convergence of Grid Computing, Web Services & Self-Managing Systems, Bloor Research.
6. Fundamentals of grid computing. IBM redpaper REDP3613
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