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dc.contributor.authorPokhrel, Ramesh
dc.date.accessioned2018-09-28T22:00:05Z
dc.date.available2018-09-28T22:00:05Z
dc.date.issued2018
dc.identifier.citationPokhrel, Ramesh. In the Quest of Trade-off between Job Parallelism and Throughput Adaptive Parameter Tuning of Hadoop. Master thesis, University of Oslo, 2018
dc.identifier.urihttp://hdl.handle.net/10852/65006
dc.description.abstractBig data is an emerging concept involving complex data sets which can give new insight and distill new knowledge. In the other hand, Hadoop MapReduce paradigm, a distributed computing software, has been adopted widely in the big data community for large-scale processing. It is known that the implementation of MapReduce with the default configuration results in less number of parallel run- ning job and thus waste of resources in the cluster during MapReduce operation. In fact, poor resource utilization and overall low performance is usually induced by the default configuration during the run-time. This thesis investigated how the cluster resources can be optimally and appropriately utilized during MapReduce operation in order to result in a better job parallelism and throughput. To achieve this, an optimal design is developed which can dynamically change the resources allocation by changing the system level parameters at run-time. The project results showed that the resources are optimally and appropriately utilized during job execution which resulted in better job parallelism and throughput.eng
dc.language.isoeng
dc.subjecttuning
dc.subjectAdaptive
dc.subjectoptimal
dc.subjectparallelism
dc.subjecthadoop
dc.subjectthroughput
dc.subjectjob
dc.titleIn the Quest of Trade-off between Job Parallelism and Throughput Adaptive Parameter Tuning of Hadoopeng
dc.typeMaster thesis
dc.date.updated2018-09-28T22:00:04Z
dc.creator.authorPokhrel, Ramesh
dc.identifier.urnURN:NBN:no-67540
dc.type.documentMasteroppgave
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/65006/1/Ramesh_Pokhrel_2018Master_Thesis_Final.pdf


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