Improving the efficiency of Map Reduce scheduling algorithm in Hadoop

Abstract-It is cost-efficient for a tenant with a limited budget to establish a virtual Map Reduce cluster by renting multiple virtual private servers (VPSs) from a VPS provider. To provide an appropriate scheduling scheme for this type of computing environment, we propose in this paper a hybrid job-driven scheduling scheme (JoSS for short) from a tenant’s perspective. Joss provides not only job-level scheduling, but also map-task level scheduling and reduce-task level scheduling. Joss classifies Map Reduce jobs based on job scale and job type and designs an appropriate scheduling policy to schedule each class of jobs. The goal is to improve da ta locality for both map tasks and reduce tasks, avoid job starvation, and improve job execution performance. Two variations of Joss are further introduced to separately achieve a better map-data locality and a faster task assignment. We conduct extensive experiments to evaluate and compare the two variations with current scheduling algorithms supported by Hadoop. The results show that the two variations outperform the other tested algorithms in terms of map-data locality, reduce-da ta locality, and network overhead without incurring significant overhead. In addition, the two variations are separately suitable for different Map Reduce-work load scenarios and provide the best job performance among all tested algorithms.