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Allocating jobs with periodic demand variations

hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierReformulations based algorithms for Combinatorial Optimization [Realopt]
dc.contributor.authorBEAUMONT, Olivier
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierReformulations based algorithms for Combinatorial Optimization [Realopt]
dc.contributor.authorBELAID, Ikbel
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierReformulations based algorithms for Combinatorial Optimization [Realopt]
dc.contributor.authorEYRAUD-DUBOIS, Lionel
hal.structure.identifierReformulations based algorithms for Combinatorial Optimization [Realopt]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorLORENZO-DEL-CASTILLO, Juan-Angel
dc.date.accessioned2024-04-04T03:18:57Z
dc.date.available2024-04-04T03:18:57Z
dc.date.issued2015-07-25
dc.date.conference2015
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/194458
dc.description.abstractEnIn the context of service hosting in large-scale datacenters, we consider the problem faced by a provider for allocating services to machines. Based on an analysis of a public Google trace correspond-ing to the use of a production cluster over a long period, we propose a model where long-running services experience demand variations with a periodic (daily) pattern and we prove that services following this model acknowledge for most of the overall CPU demand. This leads to an allo-cation problem where the classical Bin-Packing issue is augmented with the possibility to co-locate jobs whose peaks occur at different times of the day, which is bound to be more efficient than the usual approach that consist in over-provisioning for the maximum demand. In this paper, we provide a mathematical framework to analyze the packing of services exhibiting daily patterns and whose peaks occur at different times. We propose a sophisticated SOCP (Second Order Cone Program) formula-tion for this problem and we analyze how this modified packing constraint changes the behavior of standard packing heuristics (such as Best-Fit or First-Fit Decreasing). We show that taking periodicity of demand into account allows for a substantial improvement on machine utilization in the context of large-scale, state-of-the-art production datacenters.
dc.description.sponsorshipSimulation de systèmes de prochaine génération - ANR-11-INFR-0013
dc.language.isoen
dc.title.enAllocating jobs with periodic demand variations
dc.typeCommunication dans un congrès
dc.identifier.doi10.1007/978-3-662-48096-0_12
dc.subject.halInformatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleEuro-Par 2015
bordeaux.countryAT
bordeaux.conference.cityVienna
bordeaux.peerReviewedoui
hal.identifierhal-01118176
hal.version1
hal.invitednon
hal.proceedingsoui
hal.conference.organizerTräff, Jesper Larsson, Hunold, Sascha, Versaci, Francesco
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01118176v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2015-07-25&rft.au=BEAUMONT,%20Olivier&BELAID,%20Ikbel&EYRAUD-DUBOIS,%20Lionel&LORENZO-DEL-CASTILLO,%20Juan-Angel&rft.genre=unknown


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