Please use this identifier to cite or link to this item: http://nopr.niscpr.res.in/handle/123456789/44948
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dc.contributor.authorLi, Y-
dc.contributor.authorLam, T B V-
dc.contributor.authorDo, T V Van-
dc.contributor.authorChakka, R-
dc.contributor.authorRotter, C-
dc.date.accessioned2018-09-04T09:43:56Z-
dc.date.available2018-09-04T09:43:56Z-
dc.date.issued2018-09-
dc.identifier.issn0975-1084 (Online); 0022-4456 (Print)-
dc.identifier.urihttp://nopr.niscair.res.in/handle/123456789/44948-
dc.description493-498en_US
dc.description.abstractRecently, many organisations have applied Hadoop MapReduce framework for big data analytics. MapReduce applications based on the MapReduce programming model can be developed to process data of large amount. Therefore, understanding a dependency among the resource usage parameters of MapReduce applications is crucially needed from the viewpoint of cloud operators. In this paper, we analyze the inter-dependency of resource usage parameters of MapReduce applications. Autocorrelation of each resource usage parameter and correlation characteristics of each pair of resource usage parameters are investigated. Based on the analysis, we identify several groups of features that can be used to classify MapReduce applications.en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceJSIR Vol.77(09) [September 2018]en_US
dc.subjectResource Usage Parameteren_US
dc.subjectMapreduce Applicationen_US
dc.subjectAutocorrelationen_US
dc.subjectCorrelated Characteristicen_US
dc.subjectRead-Intensiveen_US
dc.subjectWrite-Intensiveen_US
dc.subjectCPU-Intensiveen_US
dc.subjectRead/Write Intensiveen_US
dc.titleInvestigation and Characterization of MapReduce Applications for Big Data Analyticsen_US
dc.typeArticleen_US
Appears in Collections:JSIR Vol.77(09) [September 2018]

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