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Improving the Eco-Efficiency of High Performance Computing Clusters Using EECluster

Author

Listed:
  • Alberto Cocaña-Fernández

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

  • Luciano Sánchez

    (Departamento de Informática, Universidad de Oviedo, 33204 Gijón, Spain
    These authors contributed equally to this work.)

  • José Ranilla

    (Departamento de Informática, Universidad de Oviedo, 33204 Gijón, Spain
    These authors contributed equally to this work.)

Abstract

As data and supercomputing centres increase their performance to improve service quality and target more ambitious challenges every day, their carbon footprint also continues to grow, and has already reached the magnitude of the aviation industry. Also, high power consumptions are building up to a remarkable bottleneck for the expansion of these infrastructures in economic terms due to the unavailability of sufficient energy sources. A substantial part of the problem is caused by current energy consumptions of High Performance Computing (HPC) clusters. To alleviate this situation, we present in this work EECluster, a tool that integrates with multiple open-source Resource Management Systems to significantly reduce the carbon footprint of clusters by improving their energy efficiency. EECluster implements a dynamic power management mechanism based on Computational Intelligence techniques by learning a set of rules through multi-criteria evolutionary algorithms. This approach enables cluster operators to find the optimal balance between a reduction in the cluster energy consumptions, service quality, and number of reconfigurations. Experimental studies using both synthetic and actual workloads from a real world cluster support the adoption of this tool to reduce the carbon footprint of HPC clusters.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:3:p:197-:d:65710
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    References listed on IDEAS

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    1. Emily B. Dennis & Byron J.T. Morgan & Martin S. Ridout, 2015. "Computational aspects of N-mixture models," Biometrics, The International Biometric Society, vol. 71(1), pages 237-246, March.
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    Cited by:

    1. 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.

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