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Stochastic Online Scheduling on Unrelated Machines

  • Varun Gupta
  • Benjamin Moseley
  • Marc UetzEmail author
  • Qiaomin Xie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10328)

Abstract

We derive the first performance guarantees for a combinatorial online algorithm that schedules stochastic, nonpreemptive jobs on unrelated machines to minimize the expectation of the total weighted completion time. Prior work on unrelated machine scheduling with stochastic jobs was restricted to the offline case, and required sophisticated linear or convex programming relaxations for the assignment of jobs to machines. Our algorithm is purely combinatorial, and therefore it also works for the online setting. As to the techniques applied, this paper shows how the dual fitting technique can be put to work for stochastic and nonpreemptive scheduling problems.

Notes

Acknowledgements

This work was done while all four authors were with the Simons Institute for the Theory of Computing at UC Berkeley. The authors wish to thank the institute for the financial support and the organizers of the semester on “Algorithms & Uncertainty” for providing a very stimulating atmosphere.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Varun Gupta
    • 1
  • Benjamin Moseley
    • 2
  • Marc Uetz
    • 3
    Email author
  • Qiaomin Xie
    • 4
  1. 1.University of ChicagoChicagoUSA
  2. 2.Washington University in St. LouisSt. LouisUSA
  3. 3.University of TwenteEnschedeNetherlands
  4. 4.University of Illinois at Urbana-ChampaignChampaignUSA

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