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dc.contributor.authorRamirez Velarde, Raúl-
dc.contributor.authorTchernykh, Andrei-
dc.contributor.authorBarba Jimenez, Carlos-
dc.contributor.authorHirales Carbajal, Adán-
dc.date.accessioned2019-10-12T00:12:19Z-
dc.date.available2019-10-12T00:12:19Z-
dc.date.created2015-04-11-
dc.date.issued2017-10-
dc.identifier.issn1572-9184-
dc.identifier.urihttps://repositorio.cetys.mx/handle/60000/91-
dc.description.abstractIn this paper, we address the problem of dynamic resource allocation in presence of job run- time uncertainty. We develop an execution delay model for runtime prediction, and design an adaptive stochastic allocation strategy, named Pareto Fractal Flow Predictor (PFFP). We conduct a comprehensive performance evaluation study of the PFFP strategy on real production traces, and compare it with other well-known non-clairvoyant strategies over two metrics. In order to choose the best strategy, we perform bi-objective analysis according to a degradation methodology. To analyze possible biasing results and negative effects of allowing a small portion of theproblem instances with large deviation to dominate the conclusions, we present performance profiles of the strategies. We show that PFFP performs well in different scenarios with a variety of workloads and distributed resources.es_ES
dc.description.sponsorshipJournal of Grid Computinges_ES
dc.language.isoen_USes_ES
dc.relation.ispartofseries15;4-
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 México*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/mx/*
dc.subjectRuntime uncertaintyes_ES
dc.subjectDistributed systemes_ES
dc.subjectResource allocationes_ES
dc.subjectSelf-similarityes_ES
dc.subjectHeavy tailses_ES
dc.titleAdaptive resource allocation with job runtime uncertaintyes_ES
dc.title.alternativeJournal of grid computinges_ES
dc.typeArticlees_ES
dc.description.urlhttps://link.springer.com/article/10.1007/s10723-017-9410-6es_ES
dc.format.page415-434es_ES
dc.identifier.indexacionACM Digital Libraryes_ES
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