Machine learning applied to accelerate energy consumption models in computing simulators

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dc.contributor.authorCastañé, Gabriel G.
dc.contributor.authorCalderón Mateos, Alejandro
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.contributor.funderMinisterio de Economía, Industria y Competitividad, Gobierno de Españaen
dc.date.accessioned2021-11-19T10:56:17Z
dc.date.available2021-11-19T10:56:17Z
dc.date.issued2019-10-16
dc.date.updated2021-11-18T16:18:44Z
dc.description.abstractThe ever-increasing growth of data centres and fog resources makes difficult for current simulation frameworks to model large computing infrastructures. Therefore, a major trade-off for simulators is the balance between abstraction level of the models, the scalability, and the performance of the executions. In order to balance better these, early forays can be found in the literature in which AI techniques are applied, but either lack of generality or are tailored to specific simulation frameworks. This paper describes the methodology to integrate memoization as a technique of supervised learning into any computing simulators framework. In this process, a bespoke kernel was constructed for the analysis of the energy models used in most well known computing simulators -cloud and fog-, but also to avoid simulation overhead. Finally, a detailed evaluation of energy models and its performance is presented showing the impact of applying supervised learning to computing simulator, showing performance improvements when models are more accurate and computations are dense.en
dc.description.sponsorshipScience Foundation Ireland (under Grant No. 12/RC/2289 P2 which is co-funded under the European Regional Development Fund);Ministerio de Economía, Industria y Competitividad, Gobierno de España (Ministry of Economy, Industry and Competitiveness under the Grant No. TIN2016-79637-P (Towards Unification of HPC and Big Data Paradigms))en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid102012en
dc.identifier.citationCastañéa, G. G. and Calderón Mateos, A. (2020) 'Machine learning applied to accelerate energy consumption models in computing simulators', Simulation Modelling Practice and Theory, 102, 102012 (16 pp). doi: 10.1016/j.simpat.2019.102012en
dc.identifier.doi10.1016/j.simpat.2019.102012en
dc.identifier.endpage16en
dc.identifier.issn1569-190X
dc.identifier.journaltitleSimulation Modelling Practice and Theoryen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/12237
dc.identifier.volume102en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S1569190X19301455
dc.rights© 2020 Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectComputer simulationen
dc.subjectMachine learningen
dc.subjectMemoizationen
dc.subjectSimulationen
dc.titleMachine learning applied to accelerate energy consumption models in computing simulatorsen
dc.typeArticle (peer-reviewed)en
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