Improving the Performance of Fair Scheduler in Hadoop

Turbocharging Hadoop Fair Scheduler using Dynamic Job Grouping in Multi-Job Workloads

“…Zhang et al [13] dynamically reconfigured Hadoop for realtime performance, outperforming the vanilla Hadoop. Lo and Cheng [14] proposed a modified fair scheduler that dynamically adjusts the resource allocation for user jobs and reduces the average turnaround time. Shi et al [15] introduced an adaptive scheduling algorithm, dynamically adjusts job priority and segments small-sized jobs, achieving a 20% reduction in the average turnaround time compared to the baseline fair scheduler.…”