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Job Scheduling in Computational Grid Using a Hybrid Algorithm Based on Particle Swarm Optimization and Extremal Optimization

Job Scheduling in Computational Grid Using a Hybrid Algorithm Based on Particle Swarm Optimization and Extremal Optimization

Tarun Kumar Ghosh, Sanjoy Das
Copyright: © 2018 |Volume: 11 |Issue: 4 |Pages: 15
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781522543237|DOI: 10.4018/JITR.2018100105
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MLA

Ghosh, Tarun Kumar, and Sanjoy Das. "Job Scheduling in Computational Grid Using a Hybrid Algorithm Based on Particle Swarm Optimization and Extremal Optimization." JITR vol.11, no.4 2018: pp.72-86. http://doi.org/10.4018/JITR.2018100105

APA

Ghosh, T. K. & Das, S. (2018). Job Scheduling in Computational Grid Using a Hybrid Algorithm Based on Particle Swarm Optimization and Extremal Optimization. Journal of Information Technology Research (JITR), 11(4), 72-86. http://doi.org/10.4018/JITR.2018100105

Chicago

Ghosh, Tarun Kumar, and Sanjoy Das. "Job Scheduling in Computational Grid Using a Hybrid Algorithm Based on Particle Swarm Optimization and Extremal Optimization," Journal of Information Technology Research (JITR) 11, no.4: 72-86. http://doi.org/10.4018/JITR.2018100105

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Abstract

Grid computing has been used as a new paradigm for solving large and complex scientific problems using resource sharing mechanism through many distributed administrative domains. One of the most challenging issues in computational Grid is efficient scheduling of jobs, because of distributed heterogeneous nature of resources. In other words, the job scheduling in computational Grid is an NP-hard problem. Thus, the use of meta-heuristic is more appropriate option in obtaining optimal results. In this article, the authors propose a novel hybrid scheduling algorithm which combines intelligently the exploration ability of Particle Swarm Optimization (PSO) with the exploitation ability of Extremal Optimization (EO) which is a recently developed local-search heuristic method. The hybrid PSO-EO reduces the schedule makespan, processing cost, and job failure rate and improves resource utilization. The proposed hybrid algorithm is compared with the standard PSO, population-based EO (PEO) and standard Genetic Algorithm (GA) methods on all these parameters. The comparison results exhibit that the proposed algorithm outperforms other three algorithms.

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