Job scheduling with adjusted runtime estimates on production supercomputers

• We studied the inaccuracy of user runtime estimates in large amount of job traces.
• We proposed a set of runtime adjusting schemes to better the estimation accuracy.
• We refined our schemes to avoid impact of too much adjusting (underestimates).
• We used real job trace to evaluate our schemes and got positive results.

The estimate of a parallel job’s running time (walltime) is an important attribute used by resource managers and job schedulers in various scenarios, such as backfilling and short-job-first scheduling. This value is provided by the user, however, and has been repeatedly shown to be inaccurate. We studied the workload characteristic based on a large amount of historical data (over 275,000 jobs in two and a half years) from a production leadership-class computer. Based on that study, we proposed a set of walltime adjustment schemes producing more accurate estimates. To ensure the utility of these schemes on production systems, we analyzed their potential impact in scheduling and evaluated the schemes with an event-driven simulator. Our experimental results show that our method can achieve not only better overall estimation accuracy but also improved overall system performance. Specifically, the average estimation accuracy of the tested workload can be improved by up to 35%, and the system performance in terms of average waiting time and weighted average waiting time can be improved by up to 22% and 28%, respectively.