17-03-2011, 02:29 PM
[attachment=10407]
Abstract
Grid computing, emerging as a new paradigm for next-generation computing, enables the sharing, selection, and aggregation of geographically distributed heterogeneous resources for solving large-scale problems in science, engineering, and commerce. The resources in the Grid are heterogeneous and geographically distributed. Availability, usage and cost policies vary depending on the particular user, time, priorities and goals. It enables the regulation of supply and demand for resources.
It provides an incentive for resource owners to participate in the Grid; and motivates the users to trade-off between deadline, budget, and the required level of quality of service. The thesis demonstrates the capability of economic-based systems for wide-area parallel and distributed computing by developing users’ quality-of-service requirements-based scheduling strategies, algorithms, and systems. It demonstrates their effectiveness by performing scheduling experiments on the World-Wide Grid for solving parameter sweep—task and data parallel—applications.
This paper focuses on introduction, grid definition.It covers about grid characteristics, types of grids and an example describing a community grid model. It gives an overview of grid tools, various components, advantages followed by conclusion.
1. INTRODUCTION:
This The Grid unites servers and storage into a single system that acts as a single computer - all your applications tap into all your computing power. Hardware resources are fully utilized and spikes in demand are met with ease. This Web site sponsored by Oracle brings you the resources you need to evaluate your organization's adoption of grid technologies. The Grid is ready when you are.
2. THE GRID:
The Grid is the computing and data management infrastructure that will provide the electronic underpinning for a global society in business, government, research, science and entertainment, integrate networking, communication, computation and information to provide a virtual platform for computation and data management in the same way that the Internet integrates resources to form a virtual platform for information. The Grid is the computing and data management infrastructure that will provide the electronic. Grid infrastructure will provide us with the ability to dynamically page link together resources as an ensemble to support the execution of large-scale, resource-intensive, and distributed applications.
Grid is a type of parallel and distributed system that enables the sharing, selection, and aggregation of geographically distributed "autonomous" resources dynamically at runtime depending on their availability, capability, performance, cost, and users' quality-of-service requirements.
What Grid Can Do?
• Exploiting underutilized resources:
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 the underutilized resources and thus has the possibility of substantially increasing the efficiency of resource usage. 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.
• Parallel CPU capacity
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 “sub jobs,” each executing on a different machine in the grid. To the extent that these sub jobs 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 sub jobs 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.
Abstract
Grid computing, emerging as a new paradigm for next-generation computing, enables the sharing, selection, and aggregation of geographically distributed heterogeneous resources for solving large-scale problems in science, engineering, and commerce. The resources in the Grid are heterogeneous and geographically distributed. Availability, usage and cost policies vary depending on the particular user, time, priorities and goals. It enables the regulation of supply and demand for resources.
It provides an incentive for resource owners to participate in the Grid; and motivates the users to trade-off between deadline, budget, and the required level of quality of service. The thesis demonstrates the capability of economic-based systems for wide-area parallel and distributed computing by developing users’ quality-of-service requirements-based scheduling strategies, algorithms, and systems. It demonstrates their effectiveness by performing scheduling experiments on the World-Wide Grid for solving parameter sweep—task and data parallel—applications.
This paper focuses on introduction, grid definition.It covers about grid characteristics, types of grids and an example describing a community grid model. It gives an overview of grid tools, various components, advantages followed by conclusion.
1. INTRODUCTION:
This The Grid unites servers and storage into a single system that acts as a single computer - all your applications tap into all your computing power. Hardware resources are fully utilized and spikes in demand are met with ease. This Web site sponsored by Oracle brings you the resources you need to evaluate your organization's adoption of grid technologies. The Grid is ready when you are.
2. THE GRID:
The Grid is the computing and data management infrastructure that will provide the electronic underpinning for a global society in business, government, research, science and entertainment, integrate networking, communication, computation and information to provide a virtual platform for computation and data management in the same way that the Internet integrates resources to form a virtual platform for information. The Grid is the computing and data management infrastructure that will provide the electronic. Grid infrastructure will provide us with the ability to dynamically page link together resources as an ensemble to support the execution of large-scale, resource-intensive, and distributed applications.
Grid is a type of parallel and distributed system that enables the sharing, selection, and aggregation of geographically distributed "autonomous" resources dynamically at runtime depending on their availability, capability, performance, cost, and users' quality-of-service requirements.
What Grid Can Do?
• Exploiting underutilized resources:
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 the underutilized resources and thus has the possibility of substantially increasing the efficiency of resource usage. 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.
• Parallel CPU capacity
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 “sub jobs,” each executing on a different machine in the grid. To the extent that these sub jobs 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 sub jobs 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.
