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Speed scaling on parallel processors with migration

Author

Listed:
  • Eric Angel

    (Université d’Évry Val d’Essonne)

  • Evripidis Bampis

    (Sorbonne Université)

  • Fadi Kacem

    (Carthage University)

  • Dimitrios Letsios

    (Technische Universität München
    Imperial College London)

Abstract

We study the problem of scheduling a set of jobs with release dates, deadlines and processing requirements (or works) on parallel speed scalable processors so as to minimize the total energy consumption. We consider that both preemptions and migrations of jobs are allowed. For this problem, there exists an optimal polynomial-time algorithm which uses as a black box an algorithm for linear programming. Here, we formulate the problem as a convex program and we propose a combinatorial polynomial-time algorithm which is based on finding maximum flows. Our algorithm runs in $$O({ nf}(n)\log U)$$ O ( nf ( n ) log U ) time, where n is the number of jobs, U is the range of all possible values of processors’ speeds divided by the desired accuracy and f(n) is the time needed for computing a maximum flow in a layered graph with O(n) vertices.

Suggested Citation

  • Eric Angel & Evripidis Bampis & Fadi Kacem & Dimitrios Letsios, 2019. "Speed scaling on parallel processors with migration," Journal of Combinatorial Optimization, Springer, vol. 37(4), pages 1266-1282, May.
    • Handle: RePEc:spr:jcomop:v:37:y:2019:i:4:d:10.1007_s10878-018-0352-0
    DOI: 10.1007/s10878-018-0352-0
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    References listed on IDEAS

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    1. Robert McNaughton, 1959. "Scheduling with Deadlines and Loss Functions," Management Science, INFORMS, vol. 6(1), pages 1-12, October.
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    Cited by:

    1. 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.
    2. Wei, Wei & Li, Haoyi & Zhang, Qinghui, 2023. "Bidirectional pruned tree-based efficient minimum cut acceleration in dense graph," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).

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