Leveraging a predictive model of the workload for intelligent slot allocation schemes in energy-efficient HPC clusters

dc.contributor.author	Cocaña Fernández, Alberto
dc.contributor.author	Sánchez Ramos, Luciano
dc.contributor.author	Ranilla Pastor, José
dc.date.accessioned	2016-05-06T09:28:11Z
dc.date.available	2016-05-06T09:28:11Z
dc.date.issued	2016
dc.identifier.citation	Engineering Applications of Artificial Intelligence, 48, p. 95-105 (2016); doi:10.1016/j.engappai.2015.10.003
dc.identifier.issn	0952-1976
dc.identifier.uri	http://hdl.handle.net/10651/37164
dc.description.sponsorship	TEC2012-38142-C04-04, FEDER, Federación Española de Enfermedades Raras; TEC2015-67387-C4-3-R, FEDER, Federación Española de Enfermedades Raras; TIN2014-56967-R, FEDER, Federación Española de Enfermedades Raras; MINECO, Federación Española de Enfermedades Raras
dc.format.extent	p. 95-105
dc.language.iso	eng
dc.relation.ispartof	Engineering Applications of Artificial Intelligence
dc.rights	©,
dc.source	Scopus
dc.source.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-84952051755&partnerID=40&md5=9f8e991f84142d9a976075b8a7c72a4b
dc.title	Leveraging a predictive model of the workload for intelligent slot allocation schemes in energy-efficient HPC clusters
dc.type	journal article
dc.identifier.doi	10.1016/j.engappai.2015.10.003
dc.relation.publisherversion	http://dx.doi.org/10.1016/j.engappai.2015.10.003

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