Achieving Best Job Performance by Increasing the Virtual MapReduce Clusters

Achieving Best Job Performance by Increasing the Virtual MapReduce Clusters
Author(s):
Matla Himagireshwar Rao, Jomma Prathap
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.
Article Details
Unique Paper ID: 144899 Publication Volume & Issue: Volume 4, Issue 6 Page(s): 82 - 85
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