Please use this identifier to cite or link to this item: http://13.232.72.61:8080/jspui/handle/123456789/348
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dc.contributor.authorKulkarni, Amogh Pramod-
dc.contributor.authorKhandewal, Mahesh-
dc.date.accessioned2018-10-01T09:23:34Z-
dc.date.available2018-10-01T09:23:34Z-
dc.date.issued2014-05-
dc.identifier.citationKulkarni, A. P., & Kulkarni, Amogh Pramod., & Khandewal, Mahesh. (2014). Survey on Hadoop and Introduction to YARN. International Journal of Emerging Technology and Advanced Engineering, 4(5), 82-87.en_US
dc.identifier.issn2250-2459-
dc.identifier.urihttp://13.232.72.61:8080/jspui/handle/123456789/348-
dc.description.abstractBig Data, the analysis of large quantities of data to gain new insight has become a ubiquitous phrase in recent years. Day by day the data is growing at a staggering rate. One of the efficient technologies that deal with the Big Data is Hadoop, which will be discussed in this paper. Hadoop, for processing large data volume jobs uses MapReduce programming model. Hadoop makes use of different schedulers for executing the jobs in parallel. The default scheduler is FIFO (First In First Out) Scheduler. Other schedulers with priority, pre-emption and non-pre-emption options have also been developed. As the time has passed the MapReduce has reached few of its limitations. So in order to overcome the limitations of MapReduce, the next generation of MapReduce has been developed called as YARN (Yet Another Resource Negotiator). So, this paper provides a survey on Hadoop, few scheduling methods it uses and a brief introduction to YARN.en_US
dc.language.isoenen_US
dc.publisherIJETAEen_US
dc.subjectInformation Scienceen_US
dc.subjectComputer simulationen_US
dc.titleSurvey on Hadoop and Introduction to YARN.en_US
dc.typeArticleen_US
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