DE Based Job Scheduling in Grid Environments

Grid Computing is a computing framework developed to meet the growing computational demands. Essential grid services contain more intelligent functions for resource management, grid service marketing, collaboration etc. The load sharing of computational jobs is the major task of computational grids. Grid resource manager provides functional mechanism for discovery, publishing of resources as well as scheduling, submission and monitoring of jobs. This paper introduces an approach, based on Differential Evolution Algorithm for scheduling jobs on computational grid. The proposed approach generates an optimal schedule which helps in completing the jobs within a minimum period of time. We evaluate the performance of our proposed approach with a direct Genetic Algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO) approach.