Probabilistic backfilling

Avi Nissimov*, Dror G. Feitelson

*Corresponding author for this work

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

11 Scopus citations

Abstract

Backfilling is a scheduling optimization that requires information about job runtimes to be known. Such information can come from either of two sources: estimates provided by users when the jobs are submitted, or predictions made by the system based on historical data regarding previous executions of jobs. In both cases, each job is assigned a precise prediction of how long it will run. We suggest that instead the whole distribution of the historical data be used. As a result, the whole backfilling framework shifts from a concrete plan for the future schedule to a probabilistic plan where jobs are backfilled based on the probability that they will terminate in time.

Original languageAmerican English
Title of host publicationJob Scheduling Strategies for Parallel Processing - 13th International Workshop, JSSPP 2007, Revised Papers
Pages102-115
Number of pages14
DOIs
StatePublished - 2008
Event13th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2007 - Seattle, WA, United States
Duration: 17 Jun 200617 Jun 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4942 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2007
Country/TerritoryUnited States
CitySeattle, WA
Period17/06/0617/06/06

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