Randomization, Speculation, and Adaptation in Batch Schedulers

Abstract

This paper proposes extensions to the backfilling job-scheduling algorithm that significantly improve its performance. We introduce variations that sort the "backfilling order" in priority-based and randomized fashions. We examine the effectiveness of guarantees present in conservative backfilling and find that initial guarantees have limited practical value, while the performance of a "no-guarantee" algorithm can be significantly better when combined with extensions that we introduce. Our study differs from many similar studies in using traces that contain user estimates. We find that actual overestimates are large and significantly different from simple models. We propose the use of speculative backfilling and speculative test runs to counteract these large overestimations. Finally, we explore the impact of dynamic, system-directed adaptation of application parallelism. The cumulative improvements of these techniques decrease the bounded slowdown, our primary metric, to less then 15% of conservative backfilling.