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Mixed Data-Parallel Scheduling for Distributed Continuous Integration

hal.structure.identifierAlgorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorBEAUMONT, Olivier
hal.structure.identifierAlgorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorBONICHON, Nicolas
hal.structure.identifierAlgorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
dc.contributor.authorCOURTÈS, Ludovic
hal.structure.identifierChercheur indépendant
dc.contributor.authorHANIN, Xavier
hal.structure.identifierComputer Engineering - Delft University of Technology [CE]
dc.contributor.authorDOLSTRA, Eelco
dc.contributor.editorIEEE
dc.date.accessioned2024-04-15T09:45:38Z
dc.date.available2024-04-15T09:45:38Z
dc.date.issued2012
dc.date.conference2012-05
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/197936
dc.description.abstractEnIn this paper, we consider the problem of schedul- ing a special kind of mixed data-parallel applications arising in the context of continuous integration. Continuous integration (CI) is a software engineering technique, which consists in re- building and testing interdependent software components as soon as developers modify them. The CI tool is able to provide quick feedback to the developers, which allows them to fix the bug soon after it has been introduced. The CI process can be described as a DAG where nodes represent package build tasks, and edges represent dependencies among these packages; build tasks themselves can in turn be run in parallel. Thus, CI can be viewed as a mixed data-parallel application. A crucial point for a successful CI process is its ability to provide quick feedback. Thus, makespan minimization is the main goal. Our contribution is twofold. First we provide and analyze a large dataset corresponding to a build DAG. Second, we compare the performance of several scheduling heuristics on this dataset.
dc.description.sponsorshipSimulation de systèmes de prochaine génération - ANR-11-INFR-0013
dc.language.isoen
dc.title.enMixed Data-Parallel Scheduling for Distributed Continuous Integration
dc.typeCommunication dans un congrès
dc.subject.halInformatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
bordeaux.hal.laboratoriesLaboratoire Bordelais de Recherche en Informatique (LaBRI) - UMR 5800*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleHeterogeneity in Computing Workshop, in IPDPS 2012
bordeaux.countryCN
bordeaux.conference.cityShangaï
bordeaux.peerReviewedoui
hal.identifierhal-00684220
hal.version1
hal.invitednon
hal.proceedingsoui
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00684220v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2012&rft.au=BEAUMONT,%20Olivier&BONICHON,%20Nicolas&COURT%C3%88S,%20Ludovic&HANIN,%20Xavier&DOLSTRA,%20Eelco&rft.genre=unknown


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