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Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques

Author

Listed:
  • Alberto Cocaña-Fernández

    (Departamento de Informática, Universidad de Oviedo, 33204 Gijón, Spain)

  • Emilio San José Guiote

    (Departamento de Informática, Universidad de Oviedo, 33204 Gijón, Spain)

  • Luciano Sánchez

    (Departamento de Informática, Universidad de Oviedo, 33204 Gijón, Spain)

  • José Ranilla

    (Departamento de Informática, Universidad de Oviedo, 33204 Gijón, Spain)

Abstract
High Performance Computing Clusters (HPCCs) are common platforms for solving both up-to-date challenges and high-dimensional problems faced by IT service providers. Nonetheless, the use of HPCCs carries a substantial and growing economic and environmental impact, owing to the large amount of energy they need to operate. In this paper, a two-stage holistic optimisation mechanism is proposed to manage HPCCs in an eco-efficiently manner. The first stage logically optimises the resources of the HPCC through reactive and proactive strategies, while the second stage optimises hardware allocation by leveraging a genetic fuzzy system tailored to the underlying equipment. The model finds optimal trade-offs among quality of service, direct/indirect operating costs, and environmental impact, through multiobjective evolutionary algorithms meeting the preferences of the administrator. Experimentation was done using both actual workloads from the Scientific Modelling Cluster of the University of Oviedo and synthetically-generated workloads, showing statistical evidence supporting the adoption of the new mechanism.

Suggested Citation

  • Alberto Cocaña-Fernández & Emilio San José Guiote & Luciano Sánchez & José Ranilla, 2019. "Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques," Energies, MDPI, vol. 12(11), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2129-:d:236926
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    References listed on IDEAS

    as
    1. Alberto Cocaña-Fernández & Luciano Sánchez & José Ranilla, 2016. "Improving the Eco-Efficiency of High Performance Computing Clusters Using EECluster," Energies, MDPI, vol. 9(3), pages 1-16, March.
    2. Maria Avgerinou & Paolo Bertoldi & Luca Castellazzi, 2017. "Trends in Data Centre Energy Consumption under the European Code of Conduct for Data Centre Energy Efficiency," Energies, MDPI, vol. 10(10), pages 1-18, September.
    3. Ni, Jiacheng & Bai, Xuelian, 2017. "A review of air conditioning energy performance in data centers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 625-640.
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