An Efficient Job Scheduling Algorithm for Computational Grid using Particle Swarm Optimization and Genetic Algorithm

Abstract

Each grid site has its own fault-tolerance strategy because each site is itself an autonomous domain. In order to provide secure fault tolerance and efficient job scheduling for grid computing, this paper introduces a new scheme which gives the solution for the fault tolerance and job scheduling problems. The proposed system can schedule the job within a minimum period of time as well as utilizing the resources in an efficient way. It also addresses the heterogeneity of fault-tolerance mechanisms in a computational grid. Here the control server which holds the genetic algorithm supports the four kinds of fault-tolerance mechanisms which include job retry, job migration without check pointing, job migration with check pointing, and job replication mechanisms. In addition to this each computational site utilizes Particle Swarm Optimization (PSO) for efficient job scheduling.