Parallel Job Scheduling under Dynamic Workloads

  • Eitan Frachtenberg
  • Dror G. Feitelson
  • Juan Fernandez
  • Fabrizio Petrini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2862)

Abstract

Jobs that run on parallel systems that use gang scheduling for multiprogramming may interact with each other in various ways. These interactions are affected by system parameters such as the level of multiprogramming and the scheduling time quantum. A careful evaluation is therefore required in order to find parameter values that lead to optimal performance. We perform a detailed performance evaluation of three factors affecting scheduling systems running dynamic workloads: multiprogramming level, time quantum, and the use of backfilling for queue management — and how they depend on offered load. Our evaluation is based on synthetic MPI applications running on a real cluster that actually implements the various scheduling schemes. Our results demonstrate the importance of both components of the gang-scheduling plus backfilling combination: gang scheduling reduces response time and slowdown, and backfilling allows doing so with a limited multiprogramming level. This is further improved by using flexible coscheduling rather than strict gang scheduling, as this reduces the constraints and allows for a denser packing.

Keywords

Cluster computing dynamic workloads job scheduling gang scheduling parallel architectures heterogeneous clusters STORM flexible coscheduling 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Eitan Frachtenberg
    • 1
    • 2
  • Dror G. Feitelson
    • 2
  • Juan Fernandez
    • 1
  • Fabrizio Petrini
    • 1
  1. 1.CCS-3 Modeling, Algorithms, and Informatics Group, Computer and Computational Sciences (CCS) DivisionLos Alamos National Laboratory (LANL) 
  2. 2.School of Computer Science and EngineeringThe Hebrew UniversityJerusalemIsrael

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