Grid Computing is basically A form of networking .Unlike conventional networks that focus on communication among devices, grid computing harnesses unused processing cycles of all computers in a network for solving problems too intensive for any stand-alone machine. The last decade has seen a substantial increase in commodity computer and network performance, mainly as a result of faster hardware and more sophisticated software. Nevertheless, there are still problems, in the fields of science, engineering, and business, which cannot be effectively dealt with using the current generation of supercomputers. In fact, due to their size and complexity, these problems are often very numerically and/or data intensive and consequently require a variety of heterogeneous resources that are not available on a single machine.
This new approach is known by several names, such as metacomputing , scalable computing, global computing, Internet computing , and more recently peer-to-peer or Grid computing. Grid Computing is a parallel processing architecture in which CPU resources are shared across a network, and all machines function as one large supercomputer. It allows unused CPU capacity in all participating machines to be allocated to one application that is extremely computation intensive and programmed for parallel processing.
The Grid MP( Meta Processor ) platform by United Devices works by amalgamating the underutilized IT resources on a corporate network into a powerful enterprise grid that can be shared by groups across the organization — even geographically disparate groups. The most common corporate technology asset, desktop PCs, are also the most underutilized, often only using 10% of their total compute power even when actively engaged in their primary business functions. By harnessing these plentiful underused computing assets and leveraging them for revenue-driving projects, the Grid MP platform provides immediate value for companies who want to move forward with their grid strategies without limiting any future grid developments.
EXAMPLE SHOWING THE PRINCIPLE:
Each yellow dot represents a computer. Each computer has one single task: add two numbers.
The trick is while adding two numbers and passing that to the next row ( 2 + 2 ) the first row can do a new calculation again while the other is busy.
And as you see the final answer does not have to be computed by one single computer. In principle this is how supers and all other parallel computers work too! You will also see that this type of computing is useless when you only have to do some simple adding.
( "With a million people you can create a road in one day, one worker needs a million days to do the same." )
A parallel processing architecture in which CPU resources are shared across a network, and all machines function as one large supercomputer.
It allows unused CPU capacity in all participating machines to beallocated to one application that is extremely computation intensive andprogrammed for parallel processing.
There Is a Lot of Idle Time
In a large enterprise, hundreds or thousands of desktop machines sit idle at any given moment. Even when a user is at the computer reading the screen and not typing or clicking, it constitutes idle time. These unused cycles can be put to use on large computational problems. Likewise, the millions of users on the Internet create a massive amount of wasted machine cycles that can be harnessed instead. This is precisely what the Search for Extraterrestrial Intelligence program does with Internet users all over the world (see SETI).
Naturally, grid computing over the Internet requires more extensive security than within a single enterprise, and robust authentication is employed in such applications.
Peer-to-Peer and Distributed Computing
Grid computing is also called "peer-to-peer computing" and "distributed computing," the latter term first coined in the 1970s, which had no relationship to this concept. Grid computing is also known as "utility computing," although that term is more widely used with third-party datacenters that supply raw computing power
The following major topics will be introduced to the readers in this chapter:
• Few functions that grid computing can do
• Meta computing
• A journey to grid computing
Few function that Grid Computing can do
When you deploy a grid, it will be to meet a set of customer requirements. To better match grid computing capabilities to those requirements, it is useful to
keep in mind the reasons for using grid computing.
1) Exploiting underutilized resources
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 percent 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.
2) Balance in resource utilization
Another function of the grid is to better balance resource utilization. An organization may have occasional unexpected peaks of activity that demandmore 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.
Metacomputing is all computing and computing-oriented activity which involves computing knowledge (science and technology) common for the research, development and application of different types of computing.
It may also deal with numerous domains of computing application, such as: industry, business, management, as well as human/cognitive factors.
Metacomputing, as a computing of computing, includes: organization of large computer networks , choice of the design criteria (for example: peer-to-peer or centralized solution) and metacomputing software (middleware, metaprogramming) development where, in the specific domains, the concept metacomputing is used as a description of software meta-layers which are networked platforms for the development of user-oriented calculations, for example for computational physics and bio-informatics.
Here, serious scientific problems of systems/networks complexity emerge , not only related to domain-dependent complexities but focused on systemic meta-complexity of computer network infrastructures.
Metacomputing refers to the general problems of computationality of the human knowledge, to the limits of the transformation of human knowledge and individual thinking to the form of computer programs. These and similar questions are also of interest of mathematical psychology.
A journey to grid computing
The irony of this is that, all hype aside, many businesses are already taking the right steps towards grid computing, simply as a part of getting their IT infrastructure under control. Many will choose to undertake this migration by starting with small-scale pilots. As more and more companies deploy clusters of industry- standard servers, IT infrastructure resembling enterprise grids will naturally result. The trick is to do things in the right order. We see three steps that companies should take on their journey to grid computing:
Standardisation on low-cost, high-density modular servers and storage based on technology such as Intel Itanium processors, blade servers, and Linux or Windows.
Consolidation of clusters of servers and storage shared among one or more data centres.
Automation of all day-to-day management tasks, enabling a single administrator to simultaneously handle hundreds of servers in clusters.
Each stage brings its own possibilities for savings and efficiencies. Gartner estimates that typical enterprises with mainframe, Unix and Windows deployments could save between 8.5 and 10.5 percent of the data centre budget by implementing standardisation, consolidation and automation (Source: ‘The Impact of RTI on IT Operations Budgets’; Donna Scott, John Oborn, Barbara Gomolski; Gartner Research Note; July 17, 2003.) And according to Giga, companies can potentially save 20 percent or more through consolidation. (Source: Giga Group, May 2003.)
Many vendors are leaping on to the grid computing bandwagon and adding to the hype surrounding it. But companies must realise that moving to grid computing is a journey, and probably not a short one. Over the next few months and years, more companies will join those who are already several steps along the way to realising the benefits that will accrue from grid computing.
Conclusion :Grid computers stand to be the new era of computers. Grid computing made solving tasks of computers play easy. Its like "With a million people you can create a road in one day, one worker needs a million days to do the same."