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Probabilistic Backfilling

  • Avi Nissimov
  • Dror G. Feitelson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4942)

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.

Keywords

Hide Markov Model User Estimate Idle Processor Runtime Distribution Previous Execution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Avi Nissimov
    • 1
  • Dror G. Feitelson
    • 1
  1. 1.Department of Computer ScienceThe Hebrew University of Jerusalem 

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