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dc.contributor.authorSYRRIS VASILEIOSen_GB
dc.contributor.authorRODRIGUEZ ASERETTO ROQUE DARIOen_GB
dc.contributor.authorSOILLE PIERREen_GB
dc.date.accessioned2017-05-05T00:16:42Z-
dc.date.available2017-01-17en_GB
dc.date.available2017-05-05T00:16:42Z-
dc.date.created2016-04-05en_GB
dc.date.issued2016en_GB
dc.date.submitted2015-11-13en_GB
dc.identifier.isbn978-92-79-56980-7en_GB
dc.identifier.issn1831-9424en_GB
dc.identifier.otherOP LB-NA-27775-EN-Nen_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC100655en_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC98347-
dc.description.abstractIn a sandbox framework for scientific computing, we deal with the task allocation problem when processing a high volume of geospatial data. A predefined meta-information catalogue guides the data selection and a profiling procedure based on data sub-sampling estimates the memory and processing requirements. The task allocation is formulated as an optimization problem conditioned by time dependencies and job execution priorities. The procedure can work as addon to any task scheduler that provides configuration options for computational resources allocation. Experiments demonstrate that the SLURM fine-tuning with the proposed method leads to better resource management and shorter schedules.en_GB
dc.description.sponsorshipJRC.I.3-Text and Data Miningen_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherPublications Office of the European Unionen_GB
dc.relation.ispartofseriesJRC98347en_GB
dc.titleTASK ALLOCATION IN HIGH PERFORMANCE PROCESSING OF GEOSPATIAL DATAen_GB
dc.typeConferences and workshops organized by the JRCen_GB
dc.identifier.doi10.2788/854791en_GB
JRC Directorate:Joint Research Centre Corporate Activities

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