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dc.contributor.authorGlushkova, Daria
dc.contributor.authorJovanovic, Petar
dc.contributor.authorAbelló Gamazo, Alberto
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació
dc.date.accessioned2018-02-01T11:02:19Z
dc.date.available2018-02-01T11:02:19Z
dc.date.issued2017
dc.identifier.citationGlushkova, D., Jovanovic, P., Abelló, A. MapReduce performance models for Hadoop 2.x. A: International Workshop On Design, Optimization, Languages and Analytical Processing of Big Data. "Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference (EDBT/ICDT 2017): Venice, Italy, March 21-24, 2017". Venice: CEUR-WS.org, 2017, p. 1-10.
dc.identifier.isbn1613-0073
dc.identifier.urihttp://hdl.handle.net/2117/113535
dc.description.abstractMapReduce is a popular programming model for distributed processing of large data sets. Apache Hadoop is one of the most common open-source implementations of such paradigm. Performance analysis of concurrent job executions has been recognized as a challenging problem, at the same time, that it may provide reasonably accurate job response time at significantly lower cost than experimental evaluation of real setups. In this paper, we tackle the challenge of defining MapReduce performance models for Hadoop 2.x. While there are several efficient approaches for modeling the performance of MapReduce workloads in Hadoop 1.x, the fundamental architectural changes of Hadoop 2.x require that the cost models are also reconsidered. The proposed solution is based on an existing performance model for Hadoop 1.x, but it takes into consideration the architectural changes of Hadoop 2.x and captures the execution flow of a MapReduce job by using queuing network model. This way the cost model adheres to the intra-job synchronization constraints that occur due the contention at shared resources. The accuracy of our solution is validated via comparison of our model estimates against measurements in a real Hadoop 2.x setup. According to our evaluation results, the proposed model produces estimates of average job response time with error within the range of 11% - 13.5%.
dc.format.extent10 p.
dc.language.isoeng
dc.publisherCEUR-WS.org
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::Informàtica::Sistemes d'informació
dc.subject.lcshElectronic data processing -- Distributed processing
dc.subject.lcshCost effectiveness
dc.subject.lcshOpen source software
dc.subject.otherMapReduce performance models
dc.subject.otherHadoop 2.x
dc.subject.otherQueuing theory
dc.subject.otherMean value analysis
dc.titleMapReduce performance models for Hadoop 2.x
dc.typeConference report
dc.subject.lemacProcessament distribuït de dades
dc.subject.lemacCost-eficàcia
dc.subject.lemacProgramari lliure
dc.contributor.groupUniversitat Politècnica de Catalunya. inSSIDE - integrated Software, Service, Information and Data Engineering
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ceur-ws.org/Vol-1810/DOLAP_paper_28.pdf
dc.rights.accessOpen Access
local.identifier.drac21874257
dc.description.versionPostprint (published version)
local.citation.authorGlushkova, D.; Jovanovic, P.; Abelló, A.
local.citation.contributorInternational Workshop On Design, Optimization, Languages and Analytical Processing of Big Data
local.citation.pubplaceVenice
local.citation.publicationNameProceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference (EDBT/ICDT 2017): Venice, Italy, March 21-24, 2017
local.citation.startingPage1
local.citation.endingPage10


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