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Energy Idle Aware Stochastic Lexicographic Local Searches for Precedence-Constraint Task List Scheduling on Heterogeneous Systems

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Listed:
  • Alejandro Santiago

    (Information Technology Engineering, Polytechnic University of Altamira, Altamira 89602, Mexico)

  • Mirna Ponce-Flores

    (División de Estudios de Posgrado, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero 89440, Mexico)

  • J. David Terán-Villanueva

    (Facultad de Ingeniería Arturo Narro Siller, Universidad Autónoma de Tamaulipas, Tampico 89140, México)

  • Fausto Balderas

    (División de Estudios de Posgrado, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero 89440, Mexico)

  • Salvador Ibarra Martínez

    (Facultad de Ingeniería Arturo Narro Siller, Universidad Autónoma de Tamaulipas, Tampico 89140, México)

  • José Antonio Castan Rocha

    (Facultad de Ingeniería Arturo Narro Siller, Universidad Autónoma de Tamaulipas, Tampico 89140, México)

  • Julio Laria Menchaca

    (Facultad de Ingeniería Arturo Narro Siller, Universidad Autónoma de Tamaulipas, Tampico 89140, México)

  • Mayra Guadalupe Treviño Berrones

    (Facultad de Ingeniería Arturo Narro Siller, Universidad Autónoma de Tamaulipas, Tampico 89140, México)

Abstract

The use of parallel applications in High-Performance Computing (HPC) demands high computing times and energy resources. Inadequate scheduling produces longer computing times which, in turn, increases energy consumption and monetary cost. Task scheduling is an NP-Hard problem; thus, several heuristics methods appear in the literature. The main approaches can be grouped into the following categories: fast heuristics, metaheuristics, and local search. Fast heuristics and metaheuristics are used when pre-scheduling times are short and long, respectively. The third is commonly used when pre-scheduling time is limited by CPU seconds or by objective function evaluations. This paper focuses on optimizing the scheduling of parallel applications, considering the energy consumption during the idle time while no tasks are executing. Additionally, we detail a comparative literature study of the performance of lexicographic variants with local searches adapted to be stochastic and aware of idle energy consumption.

Suggested Citation

  • Alejandro Santiago & Mirna Ponce-Flores & J. David Terán-Villanueva & Fausto Balderas & Salvador Ibarra Martínez & José Antonio Castan Rocha & Julio Laria Menchaca & Mayra Guadalupe Treviño Berrones, 2021. "Energy Idle Aware Stochastic Lexicographic Local Searches for Precedence-Constraint Task List Scheduling on Heterogeneous Systems," Energies, MDPI, vol. 14(12), pages 1-22, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3473-:d:573416
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    References listed on IDEAS

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    1. Fanjul-Peyro, Luis & Ruiz, Rubén, 2010. "Iterated greedy local search methods for unrelated parallel machine scheduling," European Journal of Operational Research, Elsevier, vol. 207(1), pages 55-69, November.
    2. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
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    Cited by:

    1. Fernando Ornelas & Alejandro Santiago & Salvador Ibarra Martínez & Mirna Patricia Ponce-Flores & Jesús David Terán-Villanueva & Fausto Balderas & José Antonio Castán Rocha & Alejandro H. García & Juli, 2022. "The Internet Shopping Optimization Problem with Multiple Item Units (ISHOP-U): Formulation, Instances, NP-Completeness, and Evolutionary Optimization," Mathematics, MDPI, vol. 10(14), pages 1-19, July.

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