Hadoop Performance Acceleration by Effective Data and Job Placement

In order to accelerate Hadoop performance, it is important to efficiently handle the data and job placement. More specifically, we focus on to accelerate the performance of heterogeneous distributed cluster as Hadoop default has limited performance outcome for data-intensive jobs. To improve the Hadoop performance, it is important to consider the heterogeneity of nodes, reduce job latency, and improve the data locality of blocks. In this research, we use block rearrangement policy which can rearrange the data blocks considering node’s processing capability or heterogeneity of node for data placement and effectively use node labeling and scheduling schemes for job placement to meet the goal. The experimental result shows that the proposed model accelerates the Hadoop performance by achieving high data locality and less job completion time compared to default configuration and policy.