Strategy for Tasks Scheduling in Grid Combined Neighborhood Search with Improved Adaptive Genetic Algorithm Based on Local Convergence Criterion

Abstract

Task scheduling is a key issue which must be solved in grid computing study, and a better scheduling scheme can greatly improve the efficiency of grid computing. Based on the analysis of disadvantages of adaptive genetic algorithm, the paper introduced a new local convergence criterion and its corresponding improved mutation operation. Combining with neighborhood search in mathematics task scheduling in grid was then performed. Simulation showed that this algorithm could greatly improve the performance of grid tasks scheduling.