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Performance-aware scheduling of parallel applications on non-dedicated clusters

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Arquitectura de Computadores, Comunicaciones y Sistemases
dc.contributor.authorCascajo García, Alberto
dc.contributor.authorExpósito Singh, David
dc.contributor.authorCarretero Pérez, Jesús
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2020-02-06T12:29:46Z
dc.date.available2020-02-06T12:29:46Z
dc.date.issued2019-09-02
dc.descriptionASPIDE: Exascale programIng models for extreme data processingen
dc.description.abstractThis work presents a HPC framework that provides new strategies for resource management and job scheduling, based on executing different applications in shared compute nodes, maximizing platform utilization. The framework includes a scalable monitoring tool that is able to analyze the platform's compute node utilization. We also introduce an extension of CLARISSE, a middleware for data-staging coordination and control on large-scale HPC platforms that uses the information provided by the monitor in combination with application-level analysis to detect performance degradation in the running applications. This degradation, caused by the fact that the applications share the compute nodes and may compete for their resources, is avoided by means of dynamic application migration. A description of the architecture, as well as a practical evaluation of the proposal, shows significant performance improvements up to 20% in the makespan and 10% in energy consumption compared to a non-optimized execution.en
dc.description.sponsorshipThis work was partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under the grant TIN2016-79637-P "Towards Unification of HPC and Big Data Paradigms"; and the European Union's Horizon 2020 research and innovation program under Grant No. 801091, project "Exascale programming models for extreme data processing" (ASPIDE).en
dc.format.extent21es
dc.identifier.bibliographicCitationElectronics, 2019, 8, 982, 21 pp.en
dc.identifier.doihttps://doi.org/10.3390/electronics8090982
dc.identifier.issn2079-9292
dc.identifier.publicationfirstpage1es
dc.identifier.publicationissue9 (982)es
dc.identifier.publicationlastpage21es
dc.identifier.publicationtitleElectronicsen
dc.identifier.publicationvolume8es
dc.identifier.urihttps://hdl.handle.net/10016/29648
dc.identifier.uxxiAR/0000024221
dc.language.isoengen
dc.publisherMDPIen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/801091/ASPIDEen
dc.relation.projectIDGobierno de España. TIN2016-79637-Pes
dc.rights© 2019 by the authors. Licensee MDPI, Basel, Switzerlanden
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) licenseen
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaInformáticaes
dc.subject.otherScalable toolsen
dc.subject.otherMonitoring toolsen
dc.subject.otherSchedulingen
dc.subject.otherMalleabilityen
dc.titlePerformance-aware scheduling of parallel applications on non-dedicated clustersen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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