Thermal-Aware Job Scheduling of MapReduce Applications on High Performance Clusters

In this study, we develop a thermal-aware job scheduling strategy called tDispatch tailored for MapReduce applications running on Hadoop clusters. The scheduling idea of tDispatch is motivated by a profiling study of CPU-intensive and I/O-intensive jobs from the perspective of thermal efficiency. More specifically, we investigate the thermal behaviors of these two types of jobs running on a Hadoop cluster by stress testing data nodes through extensive experiments. We show that CPU-intensive and I/O-intensive jobs exhibit various thermal and performance impacts on multicore processors and hard drives of Hadoop cluster nodes. After we quantify the thermal behaviors of Hadoop jobs on the master and data nodes of a cluster, we propose our scheduler to alternatively dispatch CPU-intensive and I/O-intensive jobs. We apply our strategy to several MapReduce applications with different resource consumption profiles. Our experimental results show that tDispatch is conducive of creating opportunities to cool down multicore processors and disks in Hadoop clusters deployed in modern data centers. Our findings can be applied in other thermal-efficient job schedulers that are aware of thermal behaviors of CPU-intensive and I/O-intensive applications submitted to Hadoop clusters.