An Efficient Distributed for Grid Resource with PH-PSO

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. There are mainly two mechanisms for acquiring information of grid resources: grid resource monitoring and grid resource prediction. 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. We present the design and evaluation of system architecture for grid resource monitoring and prediction. We discuss the key issues for system implementation, including machine learning-based methodologies for modeling and optimization of resource prediction models. Evaluations are performed on a prototype system. Our experimental results indicate that the efficiency and accuracy of our system meet the demand of online system for grid resource monitoring and prediction.