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    Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system


    Page, Andrew J. and Keane, Thomas M. and Naughton, Thomas J. (2010) Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system. Journal of Parallel and Distributed Computing, 70 (7). pp. 758-766. ISSN 0743-7315

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    Abstract

    We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms.

    Item Type: Article
    Keywords: Scheduling; Genetic algorithms; Heterogeneous; Distributed computing;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 12383
    Identification Number: https://doi.org/10.1016/j.jpdc.2010.03.011
    Depositing User: Thomas Naughton
    Date Deposited: 07 Feb 2020 15:37
    Journal or Publication Title: Journal of Parallel and Distributed Computing
    Publisher: Elsevier
    Refereed: Yes
    URI:

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