GA Based Scheduling Model for Computational Grid to Minimize Turnaround Time

Abstract

Scheduling on distributed systems is an NP hard problem and grid being a wide heterogeneous expandable system makes scheduling even a tougher job. Genetic algorithm, based on natural selection and evolution has gained popularity in recent times because of its effectiveness in handling optimization problems. In this article, a job-scheduling model for a computational grid with the objective of minimizing the turnaround time using genetic algorithm is proposed. The model evaluates various clusters in the grid to find the most suitable one with minimum turnaround time for the job-scheduling. Simulation studies compare the performance of this model with other similar models.