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Data-Locality Aware Dynamic Schedulers for Independent Tasks with Replicated Inputs

hal.structure.identifierReformulations based algorithms for Combinatorial Optimization [Realopt]
dc.contributor.authorBEAUMONT, Olivier
hal.structure.identifierReformulations based algorithms for Combinatorial Optimization [Realopt]
dc.contributor.authorLAMBERT, Thomas
hal.structure.identifierÉcole normale supérieure de Lyon [ENS de Lyon]
hal.structure.identifierOptimisation des ressources : modèles, algorithmes et ordonnancement [ROMA]
dc.contributor.authorMARCHAL, Loris
hal.structure.identifierÉcole normale supérieure - Rennes [ENS Rennes]
dc.contributor.authorTHOMAS, Bastien
dc.date.accessioned2024-04-04T03:05:09Z
dc.date.available2024-04-04T03:05:09Z
dc.date.conference2018-05-21
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/193221
dc.description.abstractEnIn this paper we concentrate on a crucial parameter for efficiency in Big Data and HPC applications: data locality. We focus on the scheduling of a set of independant tasks, each depending on an input file. We assume that each of these input files has been replicated several times and placed in local storage of different nodes of a cluster, similarly of what we can find on HDFS system for example. We consider two optimization problems, related to the two natural metrics: makespan optimization (under the constraint that only local tasks are allowed) and communication optimization (under the constraint of never letting a processor idle in order to optimize makespan). For both problems we investigate the performance of dynamic schedulers, in particular the basic greedy algorithm we can for example find in the default MapReduce scheduler. First we theoretically study its performance, with probabilistic models, and provide a lower bound for communication metric and asymptotic behaviour for both metrics. Second we propose simulations based on traces from a Hadoop cluster to compare the different dynamic schedulers and assess the expected behaviour obtained with the theoretical study.
dc.description.sponsorshipSolveurs pour architectures hétérogènes utilisant des supports d'exécution - ANR-13-MONU-0007
dc.language.isoen
dc.publisherIEEE
dc.title.enData-Locality Aware Dynamic Schedulers for Independent Tasks with Replicated Inputs
dc.typeCommunication dans un congrès
dc.identifier.doi10.1109/IPDPSW.2018.00187
dc.subject.halInformatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
bordeaux.page1-8
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleIPDPSW 2018 IEEE International Parallel and Distributed Processing Symposium Workshops
bordeaux.countryCA
bordeaux.conference.cityVancouver
bordeaux.peerReviewedoui
hal.identifierhal-01878977
hal.version1
hal.invitednon
hal.proceedingsoui
hal.conference.end2018-05-25
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01878977v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.spage=1-8&rft.epage=1-8&rft.au=BEAUMONT,%20Olivier&LAMBERT,%20Thomas&MARCHAL,%20Loris&THOMAS,%20Bastien&rft.genre=unknown


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