Achieving Best Job Performance by Increasing the Virtual MapReduce Clusters

Matla Himagireshwar Rao; Jomma Prathap

Achieving Best Job Performance by Increasing the Virtual MapReduce Clusters

Authors: Matla Himagireshwar Rao, Jomma Prathap

Unique Paper ID: 144899
Volume: 4
Issue: 6
PageNo: 82-85

Keywords: MapReduce Cluster Joss Schedulling Virtual Mapreduce Clusters

Abstract:
MapReduce job as a map function and a reduce function, and provides a runtime system to divide the job into multiple map tasks and reduce tasks and perform these tasks on a MapReduce cluster in parallel. In order to provide high map and reduce data locality, we proposed an efficient and suitable scheduling scheme named as hybrid job-driven scheduling scheme (JoSS) for the users. But, in this existing scheduling scheme, virtual MapReduce workload problem is occurred. So, in this paper we enhance this JoSS scheme work with heterogeneous virtual MapReduce clusters by providing flexibility for JoSS. In this proposed work, we are providing individual servers for individual jobs to reduce the MapReduce workload. We can achieve the high map and reduce data locality and also we can achieve the best job performance through the heterogeneous virtual Mapreduce clusters.

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Cite This Article

ISSN: 2349-6002
Volume: 4
Issue: 6
PageNo: 82-85

Achieving Best Job Performance by Increasing the Virtual MapReduce Clusters

Available:https://ijirt.org/Article?manuscript=144899

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Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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