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Energies 2016, 9(3), 197;

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

Departamento de Informática, Universidad de Oviedo, 33204 Gijón, Spain
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editor: Enrico Pontelli
Received: 31 December 2015 / Revised: 3 March 2016 / Accepted: 7 March 2016 / Published: 14 March 2016
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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. View Full-Text
Keywords: energy-efficient cluster computing; multi-criteria decision making; evolutionary algorithms energy-efficient cluster computing; multi-criteria decision making; evolutionary algorithms

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Cocaña-Fernández, A.; Sánchez, L.; Ranilla, J. Improving the Eco-Efficiency of High Performance Computing Clusters Using EECluster. Energies 2016, 9, 197.

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