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

Alberto Cocaña-Fernández, Emilio San José Guiote, Luciano Sánchez and José Ranilla
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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

Energies, 2019, vol. 12, issue 11, 1-21

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.

Keywords: energy-efficient Cluster computing; multi-criteria decision making; evolutionary algorithms (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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