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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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    1. Akiyoshi Shioura & Natalia V. Shakhlevich & Vitaly A. Strusevich, 2016. "Application of Submodular Optimization to Single Machine Scheduling with Controllable Processing Times Subject to Release Dates and Deadlines," INFORMS Journal on Computing, INFORMS, vol. 28(1), pages 148-161, February.
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    Cited by:

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    2. Alexander Kononov & Yulia Kovalenko, 2020. "Approximation algorithms for energy-efficient scheduling of parallel jobs," Journal of Scheduling, Springer, vol. 23(6), pages 693-709, December.

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