A data locality based scheduler to enhance MapReduce performance in heterogeneous environments

This paper proposes a novel data locality based scheduler which allocates input data blocks to the nodes based on their processing capacity. Also schedules map andreduce tasks to the nodes based on their computing ability in the heterogeneous Hadoop cluster. We evaluate proposed scheduler using different workloads from Hi-Bench benchmark suite. The experimental results prove that our proposed scheduler enhances the MapReduce performance in heterogeneous environments. Minimizes job execution time, and also improves data locality for different parameters as compared to the Hadoop default scheduler, Matchmaking scheduler and Delay scheduler respectively.