Abstract
Grid computing removes the limitations that exist in traditional shared computing environment, and becomes a leading trend in distributed computing system. It aggregate heterogeneous resources distributed across Internet, regardless of differences between resources such as platform, hardware, software, architecture, language, and geographical location. Such resources, which include computing, storage, data, communication bandwidth resources and other resources, are combined dynamically to form high performance computing capability of solving problems in large-scale applications. Dynamically sharing resources gives rise to resource contention. One of the challenging problems is deciding the destination nodes where the tasks of grid application are to be executed. From the perspective of system architecture, resource allocation and job scheduling are the most crucial functions of grid computing. Resource allocation and job scheduling are the core functions of grid computing. These functions are based on adequate information of available resources. Timely acquiring resource status information is of great importance in ensuring overall performance of grid computing. This work aims at building a distributed system for grid resource monitoring and prediction. Major achievements include the design and evaluation of system architecture for grid resource monitoring and prediction through Meta heuristic conditions.
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