Grid computingGrid computing is an interconnected computer systems where the machines utilize the same resources collectively. Grid computing usually consists of one main computer that distributes information and tasks to a group of networked computers to accomplish a common goal. Grid computing is often used to complete complicated or tedious mathematical or scientific calculations.Grid computing is applying the resources of many computers in a network to a single problem, also at the same time to a scientific or technical problem that requires a great number of computer processing cycles or access to large amounts of data. Grid computing is a computer network in which each computer's resources are shared with every other computer in the system. Processing power, memory and data storage are all community resources that authorized users can tap into and leverage for specific tasks. A grid computing system can be as simple as a collection of similar computers running on the same operating system or as complex as inter-networked systems comprised of every computer platform you can think of.The grid computing concept isn't a new one. It's a special kind of distributed computing. In distributed computing, different computers within the same network share one or more resources. In the ideal grid computing system, every resource is shared, turning a computer network into a powerful supercomputer. With the right user interface, accessing a grid computing system would look no different than accessing a local machine's resources. Every authorized computer would have access to enormous processing power and storage capacity.Though the concept isn't new, it's also not yet perfected. Computer scientists, programmers and engineers are still working on creating, establishing and implementing standards and protocols. Right now, many existing grid computer systems rely on proprietary software and tools. Once people agree upon a reliable set of standards and protocols, it will be easier and more efficient for organizations to adopt the grid computing model.Grid computing requires the use of software that can divide and farm out pieces of a program to as many as several thousand computers. Grid computing can be thought of as distributed and large-scale cluster computing and as a form of network-distributed parallel processing. It can be confined to the network of computer workstations within a corporation or it can be a public collaboration (in which case it is also sometimes known as a form of peer-to-peer computing).

A number of corporations, professional groups, university consortiums, and other groups have developed or are developing frameworks and software for managing grid computing projects. The European Community (EU) is sponsoring a project for a grid for high-energy physics, earth observation, and biology applications. In the United States, the National Technology Grid is prototyping a computational grid for infrastructure and an access grid for people. Sun Microsystems offers Grid Engine software. Described as a distributed resource management (DRM) tool, Grid Engine allows engineers at companies like Sony and Synopsys to pool the computer cycles on up to 80 workstations at a time. (At this scale, grid computing can be seen as a more extreme case of load balancing.)

Grid computing appears to be a promising trend for three reasons: (1) its ability to make more cost-effective use of a given amount of computer resources, (2) as a way to solve problems that can't be approached without an enormous amount of computing power, and (3) because it suggests that the resources of many computers can be cooperatively and perhaps synergistically harnessed and managed as a collaboration toward a common objective. In some grid computing systems, the computers may collaborate rather than being directed by one managing computer. One likely area for the use of grid computing will be pervasive computing applications - those in which computers pervade our environment without our necessary awareness.

Types of grid are Computational grid: This grid is used to allocate resources specifically for computing power. Scavenging grids: It is use to find and harvest machine cycles from idle servers and desktop computers for use in resource-intensive tasks. Data grids: It provides a unified interface for all data repositories in an organization, and through which data can be queried, managed and secured.

Evolution of Grid ComputingThe term Grid computing originated in the early 1990s as a metaphor for making computer power as easy to access as an electric power grid in Ian Foster and Carl Kesselmans seminal work, "The Grid: Blueprint for a new computing Infrastructure". CPU scavenging and volunteer computing were popularized beginning in 1997 by distributed net and later in 1999 by SETI@home to harness the power of networked PCs worldwide, in order to solve CPU-intensive research problems.The ideas of the grid (including those from distributed computing, object oriented programming, web services and others) were brought together by Ian Foster, Carl Kesselman and Steve Tuecke, widely regarded as the "fathers of the grid." They led the effort to create the Globus Toolkit incorporating not just computation management but also storage management, security provisioning, data movement, monitoring and a toolkit for developing additional services based on the same infrastructure including agreement negotiation, notification mechanisms, trigger services and information aggregation.

The Evolution of the Grid: The First Generation The early Grid efforts started as projects to link supercomputing sites; at this time this approach was known as metacomputing. The origin of the term is believed to have been the CASA project, one of several US Gigabit testbeds around in 1989. Larry Smarr, the former NCSA Director, is generally accredited with popularizing the term thereafter. The early to mid 1990s mark the emergence of the early metacomputing or grid environments. Typically, the objective of these early metacomputing projects was to provide computational resources to a range of high performance applications. Two representative projects in the vanguard of this type of technology were FAFNER [FAFNER] and I-WAY. These projects differ in many ways, but both had to overcome a number of similar hurdles, including communications, resource management, and the manipulation of remote data, to be able to work efficiently and effectively. The two projects also attempted to provide metacomputing resources from opposite ends of the computing spectrum. Whereas FAFNER was capable of running on any workstation with more than 4 Mbytes of memory, I-WAY was a means of unifying the resources of large US supercomputing centers.

The Evolution of the Grid: The Second Generation The emphasis of the early efforts in grid computing was in part driven by the need to link a number of US national supercomputing centers. The I-WAY project successfully achieved this goal. Today the grid infrastructure is capable of binding together more than just a few specialized supercomputing centers. A number of key enablers have helped make the Grid more ubiquitous, including the take up of high bandwidth network technologies and adoption of standards, allowing the Grid to be viewed as a viable distributed infrastructure on a global scale that can support diverse applications requiring large-scale computation and data. This vision of the Grid was presented in and we regard this as the second generation, typified by many of todays grid applications. There are three main issues that had to be confronted: Heterogeneity: a Grid involves a multiplicity of resources that are heterogeneous in nature and might span numerous administrative domains across a potentially global expanse. As any cluster manager knows, their only truly homogeneous cluster is their first one! Scalability: a Grid might grow from few resources to millions. This raises the problem of potential performance degradation as the size of a Grid increases. Consequently, applications that require a large number of geographically located resources must be designed to be latency tolerant and exploit the locality of accessed resources. Furthermore, increasing scale also involves crossing an increasing number of organizational boundaries, which emphasizes heterogeneity and the need to address authentication and trust issues. Larger scale applications may also result from the composition of other applications, which increases the intellectual complexity of systems. Adaptability: in a Grid, a resource failure is the rule, not the exception. In fact, with so many resources in a grid, the probability of some resource failing is naturally high. Resource managers or applications must tailor their behavior dynamically so that they can extract the maximum performance from the available resources and services. Middleware is generally considered to be the layer of software sandwiched between the operating system and applications, providing a variety of services required by an application to function correctly. Recently, middleware has re-emerged as a means of integrating software applications running in distributed heterogeneous environments. In a Grid, the middleware is used to hide the heterogeneous nature and provide users and applications with a homogeneous and seamless environment by providing a set of standardized interfaces to a variety of services. Setting and using standards is also key to tackling heterogeneity. Systems use varying standards and system APIs, resulting in the need to port services and applications to the plethora of computer systems used in a grid environment. As a general principle, agreed interchange formats help reduce complexity, because n converters are needed to enable n components to interoperate via one standard, as opposed to n converters for them to interoperate with each other. The Evolution of the Grid: The Third Generation The second generation provided the interoperability that was essential to achieve large-scale computation. As further grid solutions were explored, other aspects of the engineering of the Grid became apparent. In order to build new grid applications it was desirable to be able to reuse existing components and information resources, and to assemble these components in a flexible manner. The solutions involved increasing adoption of a service-oriented model and increasing attention to metadata these are two key characteristics of third generation systems. In fact the service-oriented approach itself has implications for the information fabric: the flexible assembly of grid resources into a grid application requires information about the functionality, availability and interfaces of the various components, and this information must have an agreed interpretation which can be processed by machine. For further discussion of the service oriented approach see the companion Semantic Grid paper. Whereas the Grid had traditionally been described in terms of large scale data and computation, the shift in focus in the third generation was apparent from new descriptions. In particular, the terms distributed collaboration and virtual organization were adopted in the anatomy paper. The third generation is a more holistic view of grid computing and can be said to address the infrastructure for e-Science a term which reminds us of the requirements (of doing new science, and of the e-Scientist) rather than the enabling technology. As Fox notes, the anticipated use of massively parallel computing facilities is only part of the picture that has emerged: there are also a lot more users; hence loosely coupled distributed computing has not been dominated by deployment of massively parallel machines. There is a strong sense of automation in third generation systems; for example, when humans can no longer deal with the scale and heterogeneity but delegate to processes to do so (e.g. through scripting), which leads to autonomy within the systems. This implies a need for coordination, which, in turn, needs to be specified programmatically at various levels including process descriptions. Similarly, the increased likelihood of failure implies a need for automatic recovery: configuration and repair cannot remain manual tasks. These requirements resemble the self-organizing and healing properties of biological systems, and have been termed autonomic after the autonomic nervous system. According to the definition in [Autonomic], an autonomic system has the following eight properties:

1. Needs detailed knowledge of its components and status; 2. Must configure and reconfigure itself dynamically 3. Seeks to optimize its behavior to achieve its goal 4. Is able to recover from malfunction 5. Protect itself against attack 6. Be aware of its environment 7. Implement open standards 8. Make optimized use of resources The third generation grid systems now under development are beginning to exhibit many of these features.

RELATIONSHIP BETWEEN GRID COMPUTING AND SUPER COMPUTER

Benefits of Grid Computing Grid Computing has proved beneficial in many ways, some of these benefits are: 1. Exploitation of Under-Utilized Resources: Exploitation of under-utilized resources by running an existing application on different machines, exploiting idle times on other machines and aggregating unused disk drive capacity.

2. Reduces Computational Time: Computational time is reduced for complex numerical and data analysis problems.

3. Provide Information Access: Information accessibility to maximize the exploitation of existing data assets by providing unified data access during the querying process of non-standard data formats

4. Reduces cost by optimizing existing IT infrastructure: The grid facilitate reduction of costs by optimizing the use of existing IT infrastructure investments and by enabling data sharing and distributed workflow across partners, and therefore enabling faster design processes.

5. Providing access to parallel CPU capacity: Grid computing offers potential access for large-scale parallel computation to enhance performance in computationally intensive applications.

6. Offers improved reliability: Grid technology offers alternate approach to achieving improved reliability. Parallelization can boost reliability by having multiple copies of important jobs run concurrently on separate machines on the grid. Their results can be checked for any kind of inconsistency, such as failures, data corruption and tempering.

7. Provision of resource balancing: The grid offers good resource balancing measures that can handle occasional peak loads, job prioritization, and job scheduling.

8. Effective management of resources: With grid technology, management of organization can easily visualize resource capacity and utilization to effectively control expenditures for computing resources over a larger organization. 9. Interoperability of virtual organizations: The grid offers collaboration facilities and interoperability of different virtual organization by allowing the sharing and interoperation of the heterogeneous resources available.

10. Access to additional resources: The grid offers access to other specialized devices such as the cameras and embedded systems.

11. Harnessing heterogeneous systems together: Grid computing can be used to harness heterogeneous systems together into a mega computer by applying greater computational power to a task.

12. Grid virtualization: Grid computing offers grid virtualization, thereby making a single, local computer to undertake powerful applications.