Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/12/11/2129 |
| _version_ | 1798025532565094400 |
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| author | Alberto Cocaña-Fernández Emilio San José Guiote Luciano Sánchez José Ranilla |
| author_facet | Alberto Cocaña-Fernández Emilio San José Guiote Luciano Sánchez José Ranilla |
| author_sort | Alberto Cocaña-Fernández |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T18:20:15Z |
| format | Article |
| id | doaj.art-be45371d41ea43558e9795d68f499835 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T18:20:15Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-be45371d41ea43558e9795d68f4998352022-12-22T04:09:47ZengMDPI AGEnergies1996-10732019-06-011211212910.3390/en12112129en12112129Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence TechniquesAlberto Cocaña-Fernández0Emilio San José Guiote1Luciano Sánchez2José Ranilla3Departamento de Informática, Universidad de Oviedo, 33204 Gijón, SpainDepartamento de Informática, Universidad de Oviedo, 33204 Gijón, SpainDepartamento de Informática, Universidad de Oviedo, 33204 Gijón, SpainDepartamento de Informática, Universidad de Oviedo, 33204 Gijón, SpainHigh 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.https://www.mdpi.com/1996-1073/12/11/2129energy-efficient Cluster computingmulti-criteria decision makingevolutionary algorithms |
| spellingShingle | Alberto Cocaña-Fernández Emilio San José Guiote Luciano Sánchez José Ranilla Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques Energies energy-efficient Cluster computing multi-criteria decision making evolutionary algorithms |
| title | Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques |
| title_full | Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques |
| title_fullStr | Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques |
| title_full_unstemmed | Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques |
| title_short | Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques |
| title_sort | eco efficient resource management in hpc clusters through computer intelligence techniques |
| topic | energy-efficient Cluster computing multi-criteria decision making evolutionary algorithms |
| url | https://www.mdpi.com/1996-1073/12/11/2129 |
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