Dynamic Workload Balancing for Hadoop MapReduce

Hadoop has two components which are HDFS and MapReduce. HDFS is a distributed file system for storing data for users of Hadoop and MapReduce is the framework that executes jobs from users. Hadoop stores user data based on space utilization of datanodes on the cluster rather than the processing capability of the datanodes. Furthermore Hadoop runs in a heterogeneous environment as all datanodes may not be homogeneous. For these reasons, workload imbalances will occur when Hadoop runs resulting in poor performance. In this paper, we propose a dynamic algorithm to balance the workload between different racks on a Hadoop cluster based on information obtained from analyzing the log files of Hadoop. Moving tasks from the busiest rack to another rack improves the performance of Hadoop MapReduce by reducing the running time of jobs. Our simulations indicate that using our algorithm, we can decrease by more than 50% the remaining time of the tasks belonged to a job running on the busiest rack.