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dc.contributor.authorPoggi Mastrokalo, Nicolas
dc.contributor.authorCarrera Pérez, David
dc.contributor.authorCall, Aaron
dc.contributor.authorMendoza, Sergio
dc.contributor.authorBecerra Fontal, Yolanda
dc.contributor.authorTorres Viñals, Jordi
dc.contributor.authorAyguadé Parra, Eduard
dc.contributor.authorGagliardi, Fabrizio
dc.contributor.authorLabarta Mancho, Jesús José
dc.contributor.authorReinauer, Rob
dc.contributor.authorVujic, Nikola
dc.contributor.authorGreen, Daron
dc.contributor.authorBlakeley, Jose
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.date.accessioned2015-06-03T07:19:10Z
dc.date.available2015-06-03T07:19:10Z
dc.date.created2014
dc.date.issued2014
dc.identifier.citationPoggi, N. [et al.]. ALOJA: a systematic study of Hadoop deployment variables to enable automated characterization of cost-effectiveness. A: IEEE International Conference on Big Data. "2014 IEEE International Conference on Big Data: 27-30 October 2014, Washington DC, USA: proceedings". Washington DC: Institute of Electrical and Electronics Engineers (IEEE), 2014, p. 905-913.
dc.identifier.isbn978-1-4799-5665-4
dc.identifier.urihttp://hdl.handle.net/2117/28152
dc.description.abstractThis article presents the ALOJA project, an initiative to produce mechanisms for an automated characterization of cost-effectiveness of Hadoop deployments and reports its initial results. ALOJA is the latest phase of a long-term collaborative engagement between BSC and Microsoft which, over the past 6 years has explored a range of different aspects of computing systems, software technologies and performance profiling. While during the last 5 years, Hadoop has become the de-facto platform for Big Data deployments, still little is understood of how the different layers of the software and hardware deployment options affects its performance. Early ALOJA results show that Hadoop's runtime performance, and therefore its price, are critically affected by relatively simple software and hardware configuration choices e.g., number of mappers, compression, or volume configuration. Project ALOJA presents a vendor-neutral repository featuring over 5000 Hadoop runs, a test bed, and tools to evaluate the cost-effectiveness of different hardware, parameter tuning, and Cloud services for Hadoop. As few organizations have the time or performance profiling expertise, we expect our growing repository will benefit Hadoop customers to meet their Big Data application needs. ALOJA seeks to provide both knowledge and an online service to with which users make better informed configuration choices for their Hadoop compute infrastructure whether this be on-premise or cloud-based. The initial version of ALOJA's Web application and sources are available at http://hadoop.bsc.es.
dc.description.sponsorshipThis work is partially supported by the Ministry of Science and Technology of Spain under contracts TIN2012-34557 and 2014SGR1051.
dc.format.extent9 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors::Arquitectures paral·leles
dc.subject.lcshBig data
dc.subject.lcshCloud computing
dc.subject.lcshParallel processing (Electronic computers)
dc.titleALOJA: a systematic study of Hadoop deployment variables to enable automated characterization of cost-effectiveness
dc.typeConference report
dc.subject.lemacMacrodades
dc.subject.lemacComputació en núvol
dc.subject.lemacProcessament en paral·lel (Ordinadors)
dc.contributor.groupUniversitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.identifier.doi10.1109/BigData.2014.7004322
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7004322
dc.rights.accessOpen Access
drac.iddocument15626111
dc.description.versionPostprint (author’s final draft)
upcommons.citation.authorPoggi, N.; Carrera, D.; Call, A.; Mendoza, S.; Becerra, Y.; Torres, J.; Ayguade, E.; Gagliardi, F.; Labarta, J.; Reinauer, R.; Vujic, N.; Green, D.; Blakeley, J.
upcommons.citation.contributorIEEE International Conference on Big Data
upcommons.citation.pubplaceWashington DC
upcommons.citation.publishedtrue
upcommons.citation.publicationName2014 IEEE International Conference on Big Data: 27-30 October 2014, Washington DC, USA: proceedings
upcommons.citation.startingPage905
upcommons.citation.endingPage913


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