Improving Parallel Job Scheduling Using Runtime Measurements

  • Fabricio Alves Barbosa da Silva
  • Isaac D. Scherson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1911)


We investigate the use of runtime measurements to improve job scheduling on a parallel machine. Emphasis is on gang scheduling based strategies. With the information gathered at runtime, we define a task classification scheme based on fuzzy logic and Bayesian estimators. The resulting local task classification is used to provide better service to I/O bound and interactive jobs under gang scheduling. This is achieved through the use of idle times and also by controlling the spinning time of a task in the spin block mechanism depending on the node’s workload. Simulation results show considerable improvements, in particular for I/O bound workloads, in both throughput and machine utilization for a gang scheduler using runtime information compared with gang schedulers for which this type of information is not available.


Bayesian Estimator Intensive Task Idle Slot Machine Utilization Bulk Synchronous Parallel 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Fabricio Alves Barbosa da Silva
    • 1
  • Isaac D. Scherson
    • 1
    • 2
  1. 1.Laboratoire ASIM, LIP6Université Pierre et Marie CurieParisFrance
  2. 2.Information and Comp. ScienceUniversity of CaliforniaIrvineUSA

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