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Meta-Heuristically Seeded Genetic Algorithm for Independent Job Scheduling in Grid Computing

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Applications of Evolutionary Computation (EvoApplications 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10199))

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Abstract

Grid computing is an infrastructure which connects geographically distributed computers owned by various organizations allowing their resources, such as computational power and storage capabilities, to be shared, selected, and aggregated. Job scheduling problem is one of the most difficult tasks in grid computing systems. To solve this problem efficiently, new methods are required. In this paper, a seeded genetic algorithm is proposed which uses a meta-heuristic algorithm to generate its initial population. To evaluate the performance of the proposed method in terms of minimizing the makespan, the Expected Time to Compute (ETC) simulation model is used to carry out a number of experiments. The results show that the proposed algorithm performs better than other selected techniques.

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Authors and Affiliations

  1. Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester, LE1 9BH, UK

    Muhanad Tahrir Younis, Shengxiang Yang & Benjamin Passow

Authors
  1. Muhanad Tahrir Younis
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  2. Shengxiang Yang
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  3. Benjamin Passow
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Corresponding author

Correspondence to Muhanad Tahrir Younis .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

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Younis, M.T., Yang, S., Passow, B. (2017). Meta-Heuristically Seeded Genetic Algorithm for Independent Job Scheduling in Grid Computing. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-55849-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55848-6

  • Online ISBN: 978-3-319-55849-3

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