A Fuzzy Differential Evolution Algorithm for Job Scheduling on Computational Grids

TL;DR

This paper introduces a fuzzy differential evolution algorithm designed for efficient job scheduling in computational grids, outperforming traditional methods like GA, SA, and PSO in generating optimal schedules.

Contribution

The paper presents a novel fuzzy differential evolution approach specifically tailored for job scheduling in grid computing environments, demonstrating improved performance over existing algorithms.

Findings

The fuzzy DE algorithm produces more optimal schedules than GA, SA, and fuzzy PSO.

Experimental results show significant improvements in scheduling efficiency.

The approach effectively minimizes job completion time in grid computing.

Abstract

Grid computing is the recently growing area of computing that share data, storage, computing across geographically dispersed area. This paper proposes a novel fuzzy approach using Differential Evolution (DE) for scheduling jobs on computational grids. The fuzzy based DE generates an optimal plan to complete the jobs within a minimum period of time. We evaluate the performance of the proposed fuzzy based DE algorithm with Genetic Algorithm (GA), Simulated Annealing (SA), Differential Evolution and fuzzy PSO. Experimental results have shown that the new algorithm produces more optimal solutions for the job scheduling problems compared to other algorithms.