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dc.contributor.authorSæterøy, Tim André Stunner
dc.date.accessioned2015-08-24T22:01:18Z
dc.date.issued2015
dc.identifier.citationSæterøy, Tim André Stunner. Optimizing big data cluster configurations using machine learning. Master thesis, University of Oslo, 2015
dc.identifier.urihttp://hdl.handle.net/10852/45136
dc.description.abstractThe purpose of this report is investigate the usage of algorithms aimed to optimize big data cluster configurations. Such a configuration might have many properties, and three different algorithms is presented with different approaches aimed to find an optimal configuration based on a set of rules. Presented is a suggested model and an approach based on simulations. The results from these simulations indicate that using such an approach can be viable in finding a point in a multidimensional space with dimensions representing a vector of a configuration property such as the number of CPUs or the amount of memory per node. There is more work to be done in this field of study. A model has been suggested, however the implementation and installation on a real life big data cluster is outside the scope of this report and would certainly be useful.eng
dc.language.isoeng
dc.subjectbig
dc.subjectdata
dc.subjectoptimization
dc.subjectmachine
dc.subjectlearning
dc.subjectalgorithm
dc.titleOptimizing big data cluster configurations using machine learningeng
dc.typeMaster thesis
dc.date.updated2015-08-24T22:01:18Z
dc.creator.authorSæterøy, Tim André Stunner
dc.date.embargoenddate3015-05-18
dc.rights.termsDette dokumentet er ikke elektronisk tilgjengelig etter ønske fra forfatter. Tilgangskode/Access code A
dc.identifier.urnURN:NBN:no-49205
dc.type.documentMasteroppgave
dc.rights.accessrightsclosedaccess
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/45136/1/Stery-Master.pdf


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