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

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Recent Advances in Intelligent Information Systems and Applied Mathematics (ICITAM 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 863))

Abstract

Grid computing has been treated as a new paradigm for solving large and complex scientific problems using resource sharing technique through many distributed administrative domains. The dynamic nature of Grid resources and the demands of users create challenge in the Grid scheduling problem that cannot be addressed by deterministic algorithms with polynomial time complexity. Thus, the use of meta-heuristic is more appropriate option in obtaining optimal results. The Genetic Algorithm (GA) has been proven as one of the best methods for Grid scheduling. The GA explores the problem space globally, but is sometimes unable to search locally. Thus, a hybrid algorithm is proposed which combines intelligently the GA with Particle Swarm Optimization (PSO) for the Grid job scheduling. The hybrid GA-PSO aims to reduce the schedule makespan and flowtime. The proposed hybrid algorithm is compared with the standard GA and PSO on both parameters. The comparison results exhibit that the proposed algorithm outperforms other two algorithms.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Haldia Institute of Technology, West Bengal, India

    Tarun Kumar Ghosh

  2. Department of Engineering and Technological Studies, Kalyani University, West Bengal, India

    Sanjoy Das & Nabin Ghoshal

Authors
  1. Tarun Kumar Ghosh
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  2. Sanjoy Das
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  3. Nabin Ghoshal
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Corresponding author

Correspondence to Tarun Kumar Ghosh .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  2. Haldia Institute of Technology, Haldia, West Bengal, India

    Dipak Kumar Jana

  3. Maulana Abul Kalam Azad University of Technology, Haringhata, India

    Debasis Giri

  4. Haldia Institute of Technology, Haldia, India

    Arif Ahmed

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Ghosh, T.K., Das, S., Ghoshal, N. (2020). Job Scheduling in Computational Grid Using a Hybrid Algorithm Based on Genetic Algorithm and Particle Swarm Optimization. In: Castillo, O., Jana, D., Giri, D., Ahmed, A. (eds) Recent Advances in Intelligent Information Systems and Applied Mathematics. ICITAM 2019. Studies in Computational Intelligence, vol 863. Springer, Cham. https://doi.org/10.1007/978-3-030-34152-7_66

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