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dc.contributor.authorJuarez Pérez, Fredy
dc.contributor.authorEjarque, Jorge
dc.contributor.authorBadia Sala, Rosa Maria
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2017-10-09T14:41:55Z
dc.date.available2017-10-09T14:41:55Z
dc.date.issued2018-01
dc.identifier.citationJuarez, F.; Ejarque, J.; Badia, R. M. Dynamic energy-aware scheduling for parallel task-based application in cloud computing. "Future Generation Computer Systems", Gener 2018, vol. 78, núm. 1, p. 257-271.
dc.identifier.issn0167-739X
dc.identifier.urihttp://hdl.handle.net/2117/108536
dc.description.abstractGreen Computing is a recent trend in computer science, which tries to reduce the energy consumption and carbon footprint produced by computers on distributed platforms such as clusters, grids, and clouds. Traditional scheduling solutions attempt to minimize processing times without taking into account the energetic cost. One of the methods for reducing energy consumption is providing scheduling policies in order to allocate tasks on specific resources that impact over the processing times and energy consumption. In this paper, we propose a real-time dynamic scheduling system to execute efficiently task-based applications on distributed computing platforms in order to minimize the energy consumption. Scheduling tasks on multiprocessors is a well known NP-hard problem and optimal solution of these problems is not feasible, we present a polynomial-time algorithm that combines a set of heuristic rules and a resource allocation technique in order to get good solutions on an affordable time scale. The proposed algorithm minimizes a multi-objective function which combines the energy-consumption and execution time according to the energy-performance importance factor provided by the resource provider or user, also taking into account sequence-dependent setup times between tasks, setup times and down times for virtual machines (VM) and energy profiles for different architectures. A prototype implementation of the scheduler has been tested with different kinds of DAG generated at random as well as on real task-based COMPSs applications. We have tested the system with different size instances and importance factors, and we have evaluated which combination provides a better solution and energy savings. Moreover, we have also evaluated the introduced overhead by measuring the time for getting the scheduling solutions for a different number of tasks, kinds of DAG, and resources, concluding that our method is suitable for run-time scheduling.
dc.description.sponsorshipThis work has been supported by the Spanish Government (contracts TIN2015-65316-P, TIN2012-34557, CSD2007-00050, CAC2007-00052 and SEV-2011-00067), by Generalitat de Catalunya (contract 2014-SGR-1051), by the European Commission (Euroserver project, contract 610456) and by Consejo Nacional de Ciencia y Tecnología of Mexico (special program for postdoctoral position BSC-CNS-CONACYT contract 290790, grant number 265937).
dc.format.extent15 p.
dc.language.isoeng
dc.publisherElsevier
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica
dc.subject.lcshComputer science
dc.subject.lcshParallel algorithms
dc.subject.lcshDistributed computing
dc.subject.otherDistributed Computing
dc.subject.otherCloud Computing
dc.subject.otherGreen Computing
dc.subject.otherTask-based Applications
dc.subject.otherEnergy-aware Scheduling
dc.subject.otherMulti-Heuristic Resource Allocation
dc.subject.otherMakespan
dc.subject.otherTotal Energy Flow
dc.titleDynamic energy-aware scheduling for parallel task-based application in cloud computing
dc.typeArticle
dc.subject.lemacSupercomputadors
dc.subject.lemacAlgorismes paral·lels
dc.identifier.doi10.1016/j.future.2016.06.029
dc.description.peerreviewedPeer Reviewed
dc.description.awardwinningAward-winning
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0167739X1630214X
dc.rights.accessOpen Access
local.identifier.drac28601585
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TIN2015-65316-P
local.citation.publicationNameFuture Generation Computer Systems
local.citation.volume78
local.citation.number1
local.citation.startingPage257
local.citation.endingPage271


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