Distributed System Architecture for Grid Resource Monitoring and Resource State Prediction

The core functions of grid computing are Resource allocation and job scheduling. 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. The system architecture for grid resource monitoring and prediction has been design. The key issues for system implementation, including machine learning-based methodologies for modeling and optimization of resource prediction models are discussed. Evaluations are performed on a prototype system. The experimental results indicate that the efficiency and accuracy of the system meet the demand of online system for grid resource monitoring and prediction.