Job-Length Estimation and Performance in Backfilling Schedulers

Backklling is a simple and eeective way of improving the utilization of space-sharing schedulers. Simple rst-come-rst-served approaches are ineeective because large jobs can fragment the available resources. Backklling schedulers address this problem by allowing jobs to move ahead in the queue, provided that they will not delay subsequent jobs. Previous research has shown that inaccurate estimates of execution times can lead to better backklling schedules. We characterize this eeect on several work-loads, and show that average slowdowns can be eeec-tively reduced by systematically lengthening estimated execution times. Further, we show that the average job slowdown metric can be addressed directly by sorting jobs by increasing execution time. Finally, we modify our sorting scheduler to ensure that incoming jobs can be given hard guarantees. The resulting scheduler guarantees to avoid starvation, and performs signiicantly better than previous backklling schedulers.