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dc.contributor | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
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dc.contributor | Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
dc.contributor.author | Poggi, Nicolas |
dc.contributor.author | Berral García, Josep Lluís |
dc.contributor.author | Carrera Pérez, David |
dc.date | 2016 |
dc.identifier.citation | Poggi, N., Berral, J., Carrera, D. ALOJA: A benchmarking and predictive platform for big data performance analysis. A: International Workshop on Big Data Benchmarking. "Big Data Benchmarking: 6th International Workshop, WBDB 2015: Toronto, ON, Canada, June 16-17, 2015 and 7th International Workshop, WBDB 2015: New Delhi, India, December 14-15, 2015: revised selected papers". Toronto: Springer, 2016, p. 71-84. |
dc.identifier.citation | 978-3-319-49748-8 |
dc.identifier.citation | 10.1007/978-3-319-49748-8_4 |
dc.identifier.uri | http://hdl.handle.net/2117/100159 |
dc.description.abstract | The main goals of the ALOJA research project from BSC-MSR, are to explore and automate the characterization of cost-effectivenessof Big Data deployments. The development of the project over its first year, has resulted in a open source benchmarking platform, an online public repository of results with over 42,000 Hadoop job runs, and web-based analytic tools to gather insights about system's cost-performance1. This article describes the evolution of the project's focus and research lines from over a year of continuously benchmarking Hadoop under dif- ferent configuration and deployments options, presents results, and dis cusses the motivation both technical and market-based of such changes. During this time, ALOJA's target has evolved from a previous low-level profiling of Hadoop runtime, passing through extensive benchmarking and evaluation of a large body of results via aggregation, to currently leveraging Predictive Analytics (PA) techniques. Modeling benchmark executions allow us to estimate the results of new or untested configu- rations or hardware set-ups automatically, by learning techniques from past observations saving in benchmarking time and costs. |
dc.description.abstract | This work is partially supported the BSC-Microsoft Research Centre, the Span- ish Ministry of Education (TIN2012-34557), the MINECO Severo Ochoa Research program (SEV-2011-0067) and the Generalitat de Catalunya (2014-SGR-1051). |
dc.description.abstract | Peer Reviewed |
dc.language.iso | eng |
dc.publisher | Springer |
dc.relation | http://link.springer.com/chapter/10.1007/978-3-319-49748-8_4 |
dc.relation | info:eu-repo/grantAgreement/ES/6PN/TIN2012-34557 |
dc.relation | info:eu-repo/grantAgreement/ES/1PE/TIN2012-34557 |
dc.rights | info:eu-repo/semantics/openAccess |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació |
dc.subject | Big data |
dc.subject | Data mining |
dc.subject | Database management |
dc.subject | Data mining and knowledge discovery |
dc.subject | Information storage and retrieval |
dc.subject | Information systems applications |
dc.subject | Algorithm analysis and problem complexity |
dc.subject | Simulation and modeling |
dc.subject | Macrodades |
dc.subject | Mineria de dades |
dc.title | ALOJA: A benchmarking and predictive platform for big data performance analysis |
dc.type | info:eu-repo/semantics/submittedVersion |
dc.type | info:eu-repo/semantics/conferenceObject |