Modeling user runtime estimates

User estimates of job runtimes have emerged as an important component of the workload on parallel machines, and can have a significant impact on how a scheduler treats different jobs, and thus on overall performance. It is therefore highly desirable to have a good model of the relationship between parallel jobs and their associated estimates. We construct such a model based on a detailed analysis of several workload traces. The model incorporates those features that are consistent in all of the logs, most notably the inherently modal nature of estimates (e.g. only 20 different values are used as estimates for about 90% of the jobs). We find that the behavior of users, as manifested through the estimate distributions, is remarkably similar across the different workload traces. Indeed, providing our model with only the maximal allowed estimate value, along with the percentage of jobs that have used it, yields results that are very similar to the original. The remaining difference (if any) is largely eliminated by providing information on one or two additional popular estimates. Consequently, in comparison to previous models, simulations that utilize our model are better in reproducing scheduling behavior similar to that observed when using real estimates.