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Machine Speed Scaling by Adapting Methods for Convex Optimization with Submodular Constraints

Author

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  • Akiyoshi Shioura

    (Department of Industrial Engineering and Economics, Tokyo Institute of Technology, Tokyo 152, Japan)

  • Natalia V. Shakhlevich

    (School of Computing, University of Leeds, Leeds LS2 9JT, United Kingdom)

  • Vitaly A. Strusevich

    (Department of Mathematical Sciences, University of Greenwich, Old Royal Naval College, London SE10 9LS, United Kingdom)

Abstract

In this paper, we propose a new methodology for the speed-scaling problem based on its link to scheduling with controllable processing times and submodular optimization. It results in faster algorithms for traditional speed-scaling models, characterized by a common speed/energy function. Additionally, it efficiently handles the most general models with job-dependent speed/energy functions with single and multiple machines. To the best of our knowledge, this has not been addressed prior to this study. In particular, the general version of the single-machine case is solvable by the new technique in O ( n 2 ) time.

Suggested Citation

  • Akiyoshi Shioura & Natalia V. Shakhlevich & Vitaly A. Strusevich, 2017. "Machine Speed Scaling by Adapting Methods for Convex Optimization with Submodular Constraints," INFORMS Journal on Computing, INFORMS, vol. 29(4), pages 724-736, November.
    • Handle: RePEc:inm:orijoc:v:29:y:2017:i:4:p:724-736
    DOI: 10.1287/ijoc.2017.0758
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    References listed on IDEAS

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    Cited by:

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