Modeling user runtime estimates

Dan Tsafrir*, Yoav Etsion, Dror G. Feitelson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

49 Scopus citations


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.

Original languageAmerican English
Title of host publicationJob Scheduling Strategies for Parallel Processing - 11th International Workshop, JSSPP 2005, Revised Selected Papers
Number of pages35
StatePublished - 2006
Event11th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2005 - Cambridge, MA, United States
Duration: 19 Jun 200519 Jun 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3834 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2005
Country/TerritoryUnited States
CityCambridge, MA


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