Machine Learning Strategy for Throughput Improvement in Grid Computing
Resource allocation and job scheduling are the core functions of grid computing. These functions are based on adequate information of available resources. Grid resource monitoring and grid resource prediction are the two mechanisms used. Grid resource state monitoring cares about the running state, distribution, load and malfunction of resources in grid system by means of monitoring strategies. Grid resource state prediction focuses on the variation trend and running track of resources in grid system by means of modeling and analyzing historical monitoring data. Historical information generated by monitoring and future variation generated by prediction are combined to help grid users obtain desired computing results by efficiently utilizing system resources.