02-04-2010, 03:45 PM
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 usersâ„¢ 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 sweepâ€task and data parallelâ€applications.
This paper focuses on introduction, grid definition and its evolution. 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 and bibliography.
Abstract
Data warehousing provides architectures and tools for business executives to systematically organize, understand, and use their data to make Strategic decisions. Data Warehouses are arguably among the best resources a modern company owns. As enterprises operate day in day out, the data warehouse may be updated with a myriad of business process and transactional information: orders, invoices, customers, shipments and other data together form the corporate operations archive.
As the volume of data in the Warehouse continues to grow so, the time it takes to mine (extract) the required data, individual queries, as well as loading data can consume enormous amounts of processing Power and time, impeding other data warehouse activity; and customers experiences slow response times while the information technology(IT) budget shrinks-this is the data warehouse dilemma
Ideal solutions, which perform all the work speedily and with out cost, are obviously impractical to consider. The near-ideal solution would, 1) help reduce load process time, and 2) optimize available resources for the analysis, to achieve these two tasks we need to invest in buying additional compute resources.
There is a solution, however, enabling the ability to gain compute resources without purchasing additional hardware: Grid computing.
Grid Computing provides a novel approach to harnessing distributed resources, including applications, computing platforms or databases and file systems. Applying Grid computing can drive significant benefits to the business by improving information access and responsiveness.
The Grid-enabled application layer dispatches jobs in parallel to multiple compute nodes; this parallelization of previously serial tasks to multiple CPUâ„¢s is where Grid gets its power. Grids can benefit from sharing existing resources and adding dedicated resources such as clusters to improve throughput.
Finally, Grid Computing solution enables the ability to gain compute resources with out purchasing additional hardware.
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 usersâ„¢ 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 sweepâ€task and data parallelâ€applications.
This paper focuses on introduction, grid definition and its evolution. 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 and bibliography.
Abstract
Data warehousing provides architectures and tools for business executives to systematically organize, understand, and use their data to make Strategic decisions. Data Warehouses are arguably among the best resources a modern company owns. As enterprises operate day in day out, the data warehouse may be updated with a myriad of business process and transactional information: orders, invoices, customers, shipments and other data together form the corporate operations archive.
As the volume of data in the Warehouse continues to grow so, the time it takes to mine (extract) the required data, individual queries, as well as loading data can consume enormous amounts of processing Power and time, impeding other data warehouse activity; and customers experiences slow response times while the information technology(IT) budget shrinks-this is the data warehouse dilemma
Ideal solutions, which perform all the work speedily and with out cost, are obviously impractical to consider. The near-ideal solution would, 1) help reduce load process time, and 2) optimize available resources for the analysis, to achieve these two tasks we need to invest in buying additional compute resources.
There is a solution, however, enabling the ability to gain compute resources without purchasing additional hardware: Grid computing.
Grid Computing provides a novel approach to harnessing distributed resources, including applications, computing platforms or databases and file systems. Applying Grid computing can drive significant benefits to the business by improving information access and responsiveness.
The Grid-enabled application layer dispatches jobs in parallel to multiple compute nodes; this parallelization of previously serial tasks to multiple CPUâ„¢s is where Grid gets its power. Grids can benefit from sharing existing resources and adding dedicated resources such as clusters to improve throughput.
Finally, Grid Computing solution enables the ability to gain compute resources with out purchasing additional hardware.
