Load Balancing Approach for a MapReduce Job Running on a Heterogeneous Hadoop Cluster

Hadoop MapReduce has become the de-facto standard in today's Big data world to process the more prominent data sets on a distributed cluster of commodity hardware. Today computing nodes in a commodity cluster do not have the same hardware configuration, which leads to heterogeneity. Heterogeneity has become common in the industry, research institutes, and academics. Our study shows that the current rules for calculating the required number of Reduce tasks (Reducers) for a MapReduce job are fallacious, leading to significant computing resources' overutilization. It also degrades MapReduce job performance running on a heterogeneous Hadoop cluster. However, there is no definite answer to the question: What is the optimal number of Reduce tasks required for a MapReduce job to get Hadoop's most accomplished performance in a heterogeneous cluster? We have proposed a new rule that decides the required number of reduce tasks for a MapReduce job running on a heterogeneous Hadoop cluster accurately. The proposed rule balances the load among the heterogeneous nodes in the Reduce phase of MapReduce. It also minimizes computing resources' overutilization and improves the MapReduce job execution time by an average of 18% and 28% for TeraSort and PageRank applications running on a heterogeneous Hadoop cluster.