GA-Based Task Scheduling Algorithm for Efficient Utilization of Available Resources in Computational Grid

In the grid computing environment, systematic scheduling of tasks/jobs on hand resource is the important parameter for performance evaluation of computational grid. Traditional algorithms cannot produce a load balancing schedule. In the paper, a genetic approach for grid task scheduling has been considered to achieve better solutions within a reasonable period of time. The present study aims at minimizing the make-span and flow-time at the same time and also achieves equiponderant practical application of a set of “n” available computing agents of a grid computing to get the average load balancing. The simulation results show that the proposed approach is more efficient than the GA approach reported in the literature.