CLQLMRS: improving cache locality in MapReduce job scheduling using Q-learning

Abstract

Scheduling of MapReduce jobs is an integral part of Hadoop and effective job scheduling has a direct impact on Hadoop performance. Data locality is one of the most important factors to be considered in order to improve efficiency, as it affects data transmission through the system. A number of researchers have suggested approaches for improving data locality, but few have considered cache locality. In this paper, we present a state-of-the-art job scheduler, CLQLMRS (Cache Locality with Q-Learning in MapReduce Scheduler) for improving both data locality and cache locality using reinforcement learning. The proposed algorithm is evaluated by various experiments in a heterogeneous environment. Experimental results show significantly decreased execution time compared with FIFO, Delay, and the Adaptive Cache Local scheduler.