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Analyzing Real Cluster Data for Formulating Allocation Algorithms in Cloud Platforms

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-DEL-CASTILLO, Juan-Angel
dc.date.accessioned2024-04-04T03:17:17Z
dc.date.available2024-04-04T03:17:17Z
dc.date.created2015-07
dc.date.issued2015
dc.identifier.issn0167-8191
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/194293
dc.description.abstractEnA problem commonly faced in Computer Science research is the lack of real usage data that can be used for the validation of algorithms. This situation is particularly true and crucial in Cloud Computing. The privacy of data managed by commercial Cloud infrastructures, together with their massive scale, makes them very uncommon to be available to the research community. Due to their scale, when designing resource allocation algorithms for Cloud infrastructures, many assumptions must be made in order to make the problem tractable. This paper provides deep analysis of a cluster data trace recently released by Google and focuses on a number of questions which have not been addressed in previous studies. In particular, we describe the characteristics of job resource usage in terms of dynamics (how it varies with time), of correlation between jobs (identify daily and/or weekly patterns), and correlation inside jobs between the different resources (dependence of memory usage on CPU usage). From this analysis, we propose a way to formalize the allocation problem on such platforms, which encompasses most job features from the trace with a small set of parameters.
dc.description.sponsorshipSimulation de systèmes de prochaine génération - ANR-11-INFR-0013
dc.description.sponsorshipSolveurs pour architectures hétérogènes utilisant des supports d'exécution - ANR-13-MONU-0007
dc.language.isoen
dc.publisherElsevier
dc.subject.enCloud Computing
dc.subject.enData Analysis
dc.subject.enParallel jobs
dc.title.enAnalyzing Real Cluster Data for Formulating Allocation Algorithms in Cloud Platforms
dc.typeArticle de revue
dc.subject.halInformatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
bordeaux.journalParallel Computing
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01214636
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01214636v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Parallel%20Computing&rft.date=2015&rft.eissn=0167-8191&rft.issn=0167-8191&rft.au=BEAUMONT,%20Olivier&EYRAUD-DUBOIS,%20Lionel&LORENZO-DEL-CASTILLO,%20Juan-Angel&rft.genre=article


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