GRID RESEARCH Grid Computing

Grid computing

Grid computing uses the resources of many separate computers connected by a network (usually the internet) to solve large-scale computation problems.

Grid computing offers a model for solving massive computational problems by making use of the unused resources (CPU cycles and/or disk storage) of large numbers of disparate, often desktop, computers treated as a virtual cluster embedded in a distributed telecommunications infrastructure. Grid computing's focus on the ability to support computation across administrative domains sets it apart from traditional computer clusters or traditional distributed computing.

A Grid environment is created to address resource needs; the use of that resource(s) (ie. CPU cycles, disk storage, data, software programs, peripherals, etc.) is usually characterized by its availability outside of the context of the local administrative domain. This 'external provisioning' approach entails creating a new administrative domain referred to as a Virtual Organization (VO) with a distinct and separate set of administrative policies (home administration policies plus external resource administrative policies equals the VO [aka your Grid] administrative policies). The context for a Grid 'job execution' is distinguished by the requirements created when operating outside of the home administrative context. Grid technology (aka. middleware) is employed to facilitate formalizing and complying with the Grid context associated with your application execution.

One characteristic that currently distinguishes Grid computing from distributed computing is the abstraction of a 'distributed resource' into a Grid resource. One result of abstraction is that it allows resource substitution to be more easily accomplished. Some of the overhead associated with this flexibility is reflected in the middleware layer and the temporal latency associated with the access of a Grid (or any distributed) resource. This overhead, especially the temporal latency, must be evaluated in terms of the impact on computational performance when a Grid resource is employed.


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