An Optimized Genetic Algorithm for Multi-Objective Task Scheduling in Grid Computing Environments

International Journal of Computer Science and Technology | Vol. 9, No. 5, May 2007 | pp. 33–40

DOI: 10.46882/2007/IJCST/095002

Research Article

Title: An Optimized Genetic Algorithm for Multi-Objective Task Scheduling in Grid Computing Environments

Names of Authors: Chen X. Wei¹, Sanjay Kumar²

Authors’ Affiliations: ¹School of Computer Science and Engineering, Southeast University, Nanjing, China; ²Department of Computer Science, Indian Institute of Technology, New Delhi, India

Abstract: Grid computing environments demand efficient scheduling strategies to allocate heterogeneous computational resources to complex tasks while minimizing execution time and costs. This paper presents an optimized multi-objective genetic algorithm (OMOGA) engineered to balance total makespan and monetary costs under strict resource constraints. The proposed algorithm introduces an adaptive crossover operator and a dynamic mutation rate that automatically adjust based on population fitness variance to circumvent premature convergence. Simulations conducted on standard benchmark grids demonstrate that OMOGA outperforms traditional round-robin and standard heuristic scheduling methods. Specifically, the proposed model decreased the total task execution makespan by 18.5% and lowered total economic resource usage costs by 14.2%. Statistical analysis yielded an average optimal fitness value of 0.872 ± 0.015 across 50 independent simulation runs. The algorithm demonstrates structural robustness, keeping computational complexity within O(N²) limits, where N represents the total number of independent grid tasks.

Keywords: Grid computing, task scheduling, genetic algorithm, multi-objective optimization, makespan reduction, resource allocation

Manuscript Timeline: Received February 5, 2007; Revised March 19, 2007; Accepted April 11, 2007; Published May 8, 2007

Citation: Wei, C. X., & Kumar, S. (2007). An Optimized Genetic Algorithm for Multi-Objective Task Scheduling in Grid Computing Environments. International Journal of Computer Science and Technology, 9(5), 33–40.

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