Introduction to Grid Computing

The popularity of the Internet as well as the availability of powerful computers and high-speed network technologies as low-cost commodity components is changing the way we use computers today. These technology opportunities have led to the possibility of using distributed computers as a single, unified computing resource, leading to what is popularly known as Grid computing.

Grid computing is a term referring to the combination of computer resources from multiple administrative domains to reach a common goal. The grid can be thought of as a distributed system with non-interactive workloads that involve a large number of files. What distinguishes grid computing from conventional high performance computing systems such as cluster computing is that grids tend to be more loosely coupled, heterogeneous, and geographically dispersed. Although a grid can be dedicated to a specialized application, it is more common that a single grid will be used for a variety of different purposes. Grids are often constructed with the aid of general-purpose grid software libraries known as middleware.

One of the main strategies of grid computing is to use middleware to divide and apportion pieces of a program among several computers, sometimes up to many thousands. Grid computing involves computation in a distributed fashion, which may also involve the aggregation of large-scale cluster computing-based systems.

The term Grid is chosen as an analogy to a power Grid that provides consistent, pervasive, dependable, transparent access to electricity irrespective of its source. A detailed analysis of this analogy can be found in. This new approach to network computing is known by several names, such as metacomputing, scalable computing, global computing, Internet computing, and more recently peer-to- peer (P2P) computing.

Grid Computing

Grids enable the sharing, selection, and aggregation of a wide variety of resources including supercomputers, storage systems, data sources, and specialized devices that are geographically distributed and owned by different organizations for solving large-scale computational and data intensive problems in science, engineering, and commerce. Thus creating virtual organizations and enterprises as a temporary alliance of enterprises or organizations that come together to share resources and skills, core competencies, or resources in order to better respond to business opportunities or large-scale application processing requirements, and whose cooperation is supported by computer networks.

The concept of Grid computing started as a project to link geographically dispersed supercomputers, but now it has grown far beyond its original intent. The Grid infrastructure can benefit many applications, including collaborative engineering, data exploration, high-throughput computing, and distributed supercomputing.

Grid computing, most simply stated, is distributed computing taken to the next evolutionary level. The goal is to create the illusion of a simple yet large and powerful self managing virtual computer out of a large collection of connected heterogeneous systems sharing various combinations of resources. The standardization of communications between heterogeneous systems created the Internet explosion. The emerging standardization for sharing resources, along with the availability of higher bandwidth, are driving a possibly equally large evolutionary step in grid computing.

A Grid can be viewed as a seamless, integrated computational and collaborative environment. The users interact with the Grid resource broker to solve problems, which in turn performs resource discovery, scheduling, and the processing of application jobs on the distributed Grid resources. From the end-user point of view, Grids can be used to provide the following types of services.

  • Computational services: These are concerned with providing secure services for executing application jobs on distributed computational resources individually or collectively. Resources brokers provide the services for collective use of distributed resources. A Grid providing computational services is often called a computational Grid. Some examples of computational Grids are: NASA IPG, the World Wide Grid, and the NSF TeraGrid .
  • Data services: These are concerned with proving secure access to distributed datasets and their management. To provide a scalable storage and access to the data sets, they may be replicated, catalogued, and even different datasets stored in different locations to create an illusion of mass storage. The processing of datasets is carried out using computational Grid services and such a combination is commonly called data Grids. Sample applications that need such services for management, sharing, and processing of large datasets are high-energy physics and accessing distributed chemical databases for drug design.
  • Application services: These are concerned with application management and providing access to remote software and libraries transparently. The emerging technologies such as Web services are expected to play a leading role in defining application services. They build on computational and data services provided by the Grid. An example system that can be used to develop such services is NetSolve.
  • Information services: These are concerned with the extraction and presentation of data with meaning by using the services of computational, data, and/or application services. The low-level details handled by this are the way that information is represented, stored, accessed, shared, and maintained. Given its key role in many scientific endeavors, the Web is the obvious point of departure for this level.
  • Knowledge services: These are concerned with the way that knowledge is acquired, used, retrieved, published, and maintained to assist users in achieving their particular goals and objectives. Knowledge is understood as information applied to achieve a goal, solve a problem, or execute a decision. An example of this is data mining for automatically building a new knowledge.

To build a Grid, the development and deployment of a number of services is required. These include security, information, directory, resource allocation, and payment mechanisms in an open environment and high-level services for application development, execution management, resource aggregation, and scheduling.

Grid applications (typically multidisciplinary and large-scale processing applications) often couple resources that cannot be replicated at a single site, or which may be globally located for other practical reasons. These are some of the driving forces behind the foundation of global Grids. In this light, the Grid allows users to solve larger or new problems by pooling together resources that could not be easily coupled before. Hence, the Grid is not only a computing infrastructure, for large applications, it is a technology that can bond and unify remote and diverse distributed resources ranging from meteorological sensors to data vaults and from parallel supercomputers to personal digital organizers. As such, it will provide pervasive services to all users that need them.

Grid computing can provide many benefits not available with traditional computing models:

  • Better utilization of resources: Grid computing uses distributed resources more efficiently and delivers more usable computing power. This can decrease time-to-market, allow for innovation, or enable additional testing and simulation for improved product quality. By employing existing resources, grid computing helps protect IT investments, containing costs while providing more capacity.
  • Increased user productivity: By providing transparent access to resources, work can be completed more quickly. Users gain additional productivity as they can focus on design and development rather than wasting valuable time hunting for resources and manually scheduling and managing large numbers of jobs.
  • Scalability: Grids can grow seamlessly over time, allowing many thousands of processors to be integrated into one cluster. Components can be updated independently and additional resources can be added as needed, reducing large one-time expenses.
  • Flexibility: Grid computing provides computing power where it is needed most, helping to better meet dynamically changing work loads. Grids can contain heterogeneous compute nodes, allowing resources to be added and removed as needs dictate.

To conclude, Grid computing is a technology used to harness computing powers from various sources and use them in harmony to achieve a specific goal. The great advantage of grid computing is the ability to significantly reduce the time that is taken to accomplish that goal, thereby increasing efficiency. Most applications of grid computing are ones where the computing resources of one computing unit prove to be insufficient for the task at hand. The computing unit in question could potentially range from a single personal computer to a supercomputer within a large organization.

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