Genetic Algorithm Based Dynamic Jobs Scheduling in grid computing

Abstract

A computational grid is a large scale, heterogeneous collection of autonomous systems, geographically distributed and interconnected by low latency and high bandwidth networks. The sharing of computational resources is a major aspect of grids. Scheduling is a key problem in emergent computational systems, such as Grid and P2P, in order to benefit from the large computing capaci ty of such systems. Our approach is to dynamically generate an optimal schedule so as to complete the different tasks in a minimum period of time as well as utilizing the resources in an efficient way. There are so many approaches for scheduling like Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony optimization (ACO) and Particle Swarm Optimization (PSO) Algorithm. In this paper, We would like to present Genetic Algorithms (GAs) based schedulers for efficiently a l l o c a t ing jobs to resources in a Grid system. We would also like to implement GAs for designing efficient Grid schedulers when makespan is minimized. Our GAbased schedulers are very fast and hence they can be used schedule jobs arrived in the Grid system.