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dc.contributor.authorLérida Monsó, Josep Lluís
dc.contributor.authorSolsona Tehàs, Francesc
dc.contributor.authorHernandez, Porfidio
dc.contributor.authorGiné, Francesc
dc.contributor.authorHanzich, Mauricio
dc.contributor.authorConde Colom, Josep
dc.date.accessioned2016-05-20T15:53:07Z
dc.date.issued2013
dc.identifier.issn0743-7315
dc.identifier.urihttp://hdl.handle.net/10459.1/57075
dc.description.abstractThe abundant computing resources in current organizations provide new opportunities for executing parallel scientific applications and using resources. The Enterprise Desktop Grid Computing (EDGC) paradigm addresses the potential for harvesting the idle computing resources of an organization’s desktop PCs to support the execution of the company’s large-scale applications. In these environments, the accuracy of response-time predictions is essential for effective metascheduling that maximizes resource usage without harming the performance of the parallel and local applications. However, this accuracy is a major challenge due to the heterogeneity and non-dedicated nature of EDGC resources. In this paper, two new prediction techniques are presented based on the state of resources. A thorough analysis by linear regression demonstrated that the proposed techniques capture the real behavior of the parallel applications better than other common techniques in the literature. Moreover, it is possible to reduce deviations with a proper modeling of prediction errors, and thus, a Self-adjustable Correction method (SAC) for detecting and correcting the prediction deviations was proposed with the ability to adapt to the changes in load conditions. An extensive evaluation in a real environment was conducted to validate the SAC method. The results show that the use of SAC increases the accuracy of response-time predictions by 35%. The cost of predictions with self-correction and its accuracy in a real environment was analyzed using a combination of the proposed techniques. The results demonstrate that the cost of predictions is negligible and the combined use of the prediction techniques is preferable.ca_ES
dc.description.sponsorshipThis work was supported by the Spanish Ministry of Science and Technology under grant No. TIN2011-28689-C02-02 and No. TIN2010-18978.ca_ES
dc.language.isoengca_ES
dc.publisherElsevierca_ES
dc.relationMICINN/PN2008-2011/TIN2010-18978
dc.relationMICINN/PN2008-2011/TIN2011-28689-C02-02ca_ES
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.1016/j.jpdc.2013.02.007ca_ES
dc.relation.ispartofJournal of Parallel and Distributed Computing, 2013, vol. 73, núm. 6, p. 777-789ca_ES
dc.rights(c) Elsevier, 2013ca_ES
dc.subjectSystem-generated predictionsca_ES
dc.subjectInstance-based learningca_ES
dc.subjectApplication modelingca_ES
dc.titleState-based predictions with self-correction on Enterprise Desktop Grid environmentsca_ES
dc.typearticleca_ES
dc.identifier.idgrec019898
dc.type.versionpublishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccessca_ES
dc.identifier.doihttps://doi.org/10.1016/j.jpdc.2013.02.007
dc.date.embargoEndDate2025-01-01


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