Improving straggler task performance in a heterogeneous MapReduce framework using reinforcement learning. (2018) - Naik, Nenavath Srinivas; Negi, Atul; Sastry, V.N.
MapReduce is one of the most significant distributed and parallel processing frameworks for large-scale data-intensive jobs proposed in recent times. Intelligent scheduling decisions can potentially help in significantly reducing the overall runtime of jobs. It is observed that the total time to completion of a job gets extended because of some slow tasks. Especially in heterogeneous environments, the job completion times do not synchronise. As originally conceived, MapReduce default scheduler was not very effective about slow task identification. In the literature, longest approximate time to end (LATE) scheduler extends to the heterogeneous environment, but it has limitations in properly estimating the progress of the tasks. It takes a static view of the task progress. In this paper, we propose a novel reinforcement learning-based MapReduce scheduler for heterogeneous environments called MapReduce reinforcement learning (MRRL) scheduler. It observes the system state of task execution and suggests speculative re-execution of the slower tasks to available nodes in the heterogeneous cluster without assuming any prior knowledge of the environmental characteristics. We observe that the experimental results show consistent improvements in performance as compared to the LATE and Hadoop default schedulers for different workloads of the Hi-bench benchmark suite.