Addressing Performance Heterogeneity in MapReduce Clusters with Elastic Tasks

Abstract

MapReduce applications, which require access to a large number of computing nodes, are commonly deployed in heterogeneous environments. The performance discrepancy between individual nodes in a heterogeneous cluster present significant challenges to attain good performance in MapReduce jobs. MapReduce implementations designed and optimized for homogeneous environments perform poorly on heterogeneous clusters. We attribute suboptimal performance in heterogeneous clusters to significant load imbalance between map tasks. We identify two MapReduce designs that hinder load balancing: (1) static binding between mappers and their data makes it difficult to exploit data redundancy for load balancing; (2) uniform map sizes is not optimal for nodes with heterogeneous performance. To address these issues, we propose FlexMap, a user-transparent approach that dynamically provisions map tasks to match distinct machine capacity in heterogeneous environments. We implemented FlexMap in Hadoop-2.6.0. Experimental results show that it reduces job completion time by as much as 40% compared to stock Hadoop and 30% to SkewTune.