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Stochastic Online Scheduling on Parallel Machines

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Approximation and Online Algorithms (WAOA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3351))

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Abstract

We consider a non-preemptive, stochastic parallel machine scheduling model with the goal to minimize the weighted completion times of jobs. In contrast to the classical stochastic model where jobs with their processing time distributions are known beforehand, we assume that jobs appear one by one, and every job must be assigned to a machine online. We propose a simple online scheduling policy for that model, and prove a performance guarantee that matches the currently best known performance guarantee for stochastic parallel machine scheduling. For the more general model with job release dates we derive an analogous result, and for NBUE distributed processing times we even improve upon the previously best known performance guarantee for stochastic parallel machine scheduling. Moreover, we derive some lower bounds on approximation.

Research partially supported by the DFG Research Center ”Mathematics for key technologies” (FZT 86) in Berlin.

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Author information

Authors and Affiliations

  1. Institut für Mathematik, Technische Universität Berlin, Strasse des 17. Juni 136, 10623, Berlin, Germany

    Nicole Megow

  2. Department of Quantitative Economics, Maastricht University, P.O.Box 616, 6200 MD, Maastricht, The Netherlands

    Marc Uetz

  3. Department Optimization, Konrad-Zuse-Zentrum für Informationstechnik Berlin, Takustr. 7, 14195, Berlin, Germany

    Tjark Vredeveld

Authors
  1. Nicole Megow
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  2. Marc Uetz
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  3. Tjark Vredeveld
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Editor information

Editors and Affiliations

  1. Dipartimento di Informatica ed Applicazioni, Università di Salerno, 84084, Fisciano, (SA), Italy

    Giuseppe Persiano

  2. Department of Computer Science, The University of Western Ontario, London, Canada

    Roberto Solis-Oba

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© 2005 Springer-Verlag Berlin Heidelberg

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Megow, N., Uetz, M., Vredeveld, T. (2005). Stochastic Online Scheduling on Parallel Machines. In: Persiano, G., Solis-Oba, R. (eds) Approximation and Online Algorithms. WAOA 2004. Lecture Notes in Computer Science, vol 3351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11389811_15

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  • DOI: https://doi.org/10.1007/11389811_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24574-2

  • Online ISBN: 978-3-540-31833-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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