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Pareto Approximation Empirical Results of Energy-Aware Optimization for Precedence-Constrained Task Scheduling Considering Switching Off Completely Idle Machines

Author

Listed:
  • José Antonio Castán Rocha

    (Faculty of Engineering Tampico, Autonomous University of Tamaulipas, Centro Universitario Sur, Tampico 89109, Mexico)

  • Alejandro Santiago

    (Faculty of Engineering Tampico, Autonomous University of Tamaulipas, Centro Universitario Sur, Tampico 89109, Mexico)

  • Alejandro H. García-Ruiz

    (Faculty of Engineering Tampico, Autonomous University of Tamaulipas, Centro Universitario Sur, Tampico 89109, Mexico)

  • Jesús David Terán-Villanueva

    (Faculty of Engineering Tampico, Autonomous University of Tamaulipas, Centro Universitario Sur, Tampico 89109, Mexico)

  • Salvador Ibarra Martínez

    (Faculty of Engineering Tampico, Autonomous University of Tamaulipas, Centro Universitario Sur, Tampico 89109, Mexico)

  • Mayra Guadalupe Treviño Berrones

    (Faculty of Engineering Tampico, Autonomous University of Tamaulipas, Centro Universitario Sur, Tampico 89109, Mexico)

Abstract

Recent advances in cloud computing, large language models, and deep learning have started a race to create massive High-Performance Computing (HPC) centers worldwide. These centers increase in energy consumption proportionally to their computing capabilities; for example, according to the top 500 organization, the HPC centers Frontier, Aurora, and Super Computer Fugaku report energy consumptions of 22,786 kW, 38,698 kW, and 29,899 kW, respectively. Currently, energy-aware scheduling is a topic of interest to many researchers. However, as far as we know, this work is the first approach considering the idle energy consumption by the HPC units and the possibility of turning off unused units entirely, driven by a quantitative objective function. We found that even when turning off unused machines, the objectives of makespan and energy consumption still conflict and, therefore, their multi-objective optimization nature. This work presents empirical results for AGEMOEA, AGEMOEA2, GWASFGA, MOCell, MOMBI, MOMBI2, NSGA2, and SMS-EMOA. The best-performing algorithm is MOCell for the 400 real scheduling problem tests. In contrast, the best-performing algorithm is GWASFGA for a small-instance synthetic testbed.

Suggested Citation

  • José Antonio Castán Rocha & Alejandro Santiago & Alejandro H. García-Ruiz & Jesús David Terán-Villanueva & Salvador Ibarra Martínez & Mayra Guadalupe Treviño Berrones, 2024. "Pareto Approximation Empirical Results of Energy-Aware Optimization for Precedence-Constrained Task Scheduling Considering Switching Off Completely Idle Machines," Mathematics, MDPI, vol. 12(23), pages 1-53, November.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:23:p:3733-:d:1531178
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    References listed on IDEAS

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    1. Beume, Nicola & Naujoks, Boris & Emmerich, Michael, 2007. "SMS-EMOA: Multiobjective selection based on dominated hypervolume," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1653-1669, September.
    2. Ana Ruiz & Rubén Saborido & Mariano Luque, 2015. "A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm," Journal of Global Optimization, Springer, vol. 62(1), pages 101-129, May.
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