Energy efficient scheduling of MapReduce workloads on heterogeneous clusters

Energy efficiency has become the center of attention in emerging data center infrastructures as increasing energy costs continue to outgrow all other operating expenditures. In this work we investigate energy aware scheduling heuristics to increase the energy efficiency of MapReduce workloads on heterogeneous Hadoop clusters comprising both low power (wimpy) and high performance (brawny) nodes. We first make a case for heterogeneity by showing that low power Intel Atom processors and high performance Intel Sandy Bridge processors are more energy efficient for I/O bound workloads and CPU bound workloads, respectively. Then we present several energy efficient scheduling heuristics that exploit this heterogeneity and real-time power measurements enabled by modern processor architectures. Through experiments on a 23-node heterogeneous Hadoop cluster we demonstrate up to 27% better energy efficiency with our heuristics compared with the default Hadoop scheduler.