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MREv: An Automatic MapReduce Evaluation Tool for Big Data Workloads
dc.contributor.author | Veiga, Jorge | |
dc.contributor.author | Expósito, Roberto R. | |
dc.contributor.author | Taboada, Guillermo L. | |
dc.contributor.author | Touriño, Juan | |
dc.date.accessioned | 2024-02-29T15:57:13Z | |
dc.date.available | 2024-02-29T15:57:13Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Jorge Veiga, Roberto R. Expósito, Guillermo L. Taboada, Juan Touriño, “MREv: An Automatic MapReduce Evaluation Tool for Big Data Workloads”, in Procedia Computer Science, V. 51, 2015, p. 80-89, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2015.05.202. | es_ES |
dc.identifier.issn | 1877-0509 | |
dc.identifier.uri | http://hdl.handle.net/2183/35751 | |
dc.description.abstract | [Abstract]: The popularity of Big Data computing models like MapReduce has caused the emergence of many frameworks oriented to High Performance Computing (HPC) systems. The suitability of each one to a particular use case depends on its design and implementation, the underlying system resources and the type of application to be run. Therefore, the appropriate selection of one of these frameworks generally involves the execution of multiple experiments in order to assess their performance, scalability and resource efficiency. This work studies the main issues of this evaluation, proposing a new MapReduce Evaluator (MREv) tool which unifies the configuration of the frameworks, eases the task of collecting results and generates resource utilization statistics. Moreover, a practical use case is described, including examples of the experimental results provided by this tool. MREv is available to download at http://mrev.des.udc.es. | es_ES |
dc.description.sponsorship | This work was funded by the Ministry of Economy and Competitiviness of Spain (Ref. TIN2013- 42148-P), and by the Galician Government (Xunta de Galicia) under the Consolidation Program of Competitive Reference Groups (Ref. GRC2013/055), cofunded by FEDER funds of the EU. | es_ES |
dc.description.sponsorship | Xunta de Galicia; GRC2013/055 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2013-42148-P/ES/NUEVOS DESAFIOS EN COMPUTACION DE ALTAS PRESTACIONES: DESDE ARQUITECTURAS HASTA APLICACIONES | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.procs.2015.05.202 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | High Performance Computing (HPC) | es_ES |
dc.subject | Big Data | es_ES |
dc.subject | MapReduce | es_ES |
dc.subject | Performance Evaluation | es_ES |
dc.subject | Resource Efficiency | es_ES |
dc.subject | InfiniBand | es_ES |
dc.title | MREv: An Automatic MapReduce Evaluation Tool for Big Data Workloads | es_ES |
dc.type | info:eu-repo/semantics/conference paper | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Procedia Computer Science | es_ES |
UDC.volume | 51 | es_ES |
UDC.startPage | 80 | es_ES |
UDC.endPage | 89 | es_ES |
dc.identifier.doi | 10.1016/j.procs.2015.05.202 | |
UDC.conferenceTitle | International Conference On Computational Science - ICCS 2015 |