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hal.structure.identifierReformulations based algorithms for Combinatorial Optimization [Realopt]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorBEAUMONT, Olivier
hal.structure.identifierReformulations based algorithms for Combinatorial Optimization [Realopt]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorEYRAUD-DUBOIS, Lionel
hal.structure.identifierReformulations based algorithms for Combinatorial Optimization [Realopt]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorLORENZO, Juan-Angel
hal.structure.identifierChalmers University of Technology [Göteborg]
dc.contributor.authorRENAUD-GOUD, Paul
dc.date.accessioned2024-04-04T02:17:08Z
dc.date.available2024-04-04T02:17:08Z
dc.date.created2014-05-28
dc.date.issued2014-12
dc.date.conference2014-12
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/189200
dc.description.abstractEnWe consider robust resource allocation of services in Clouds. More specifically, we consider the case of a large public or private Cloud platform such that a relatively small set of large and independent services accounts for most of the over-all CPU usage of the platform. We will show, using a recent trace from Google, that this assumption is very reasonable in practice. The objective is to provide an allocation of the services onto the machines of the platform, using replication in order to be resilient to machine failures. The services are characterized by their demand along several dimensions (CPU, memory,. . .) and by their quality of service require-ments, that have been defined through an SLA in the case of a public Cloud or fixed by the administrator in the case of a private Cloud. This quality of service defines the required robustness of the service, by setting an upper limit on the probability that the provider fails to allocate the required quantity of resources. This maximum probability of failure can be transparently turned into a set of (price, penalty) pairs. Our contribution is two-fold. First, we propose a formal model for this allocation problem, and we justify our as-sumptions based on an analysis of a publicly available clus-ter usage trace from Google. Second, we propose a resource allocation strategy whose complexity is low in the number of resources, what makes it well suited to large platforms. Finally, we provide an analysis of the proposed strategy through an extensive set of simulations, showing that it can be succesfully applied in the context of the Google trace.
dc.description.sponsorshipSimulation de systèmes de prochaine génération - ANR-11-INFR-0013
dc.language.isoen
dc.publisherIEEE
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enresilience
dc.subject.enreplication
dc.subject.enCloud Computing
dc.subject.enresource allocation
dc.subject.enreliability
dc.subject.encolumn generation
dc.subject.enprobability estimation
dc.subject.enCloud
dc.subject.enfailure
dc.subject.enservice
dc.subject.enallocation
dc.subject.enbin packing
dc.subject.enlinear program
dc.subject.enmemory
dc.subject.enCPU
dc.subject.enlarge scale
dc.subject.enprobability estimate
dc.title.enEfficient and Robust Allocation Algorithms in Clouds under Memory Constraints
dc.typeCommunication dans un congrès
dc.subject.halInformatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
bordeaux.page12
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleIEEE International Conference on High Performance Computing (HiPC 2014)
bordeaux.countryIN
bordeaux.conference.cityGoa
bordeaux.peerReviewedoui
hal.identifierhal-00874936
hal.version1
hal.invitednon
hal.proceedingsoui
hal.conference.end2014-12
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00874936v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2014-12&rft.spage=12&rft.epage=12&rft.au=BEAUMONT,%20Olivier&EYRAUD-DUBOIS,%20Lionel&LORENZO,%20Juan-Angel&RENAUD-GOUD,%20Paul&rft.genre=unknown


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