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Restricted Adaptivity in Stochastic Scheduling

Authors Guillaume Sagnol , Daniel Schmidt genannt Waldschmidt

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Author Details

Guillaume Sagnol
  • Institut für Mathematik, TU Berlin, Germany
Daniel Schmidt genannt Waldschmidt
  • Institut für Mathematik, TU Berlin, Germany

Funding

Guillaume Sagnol and Daniel Schmidt genannt Waldschmidt were funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – The Berlin Mathematics Research Center MATH+ (EXC-2046/1, project ID: 390685689).

Acknowledgements

We thank Thibault Juillard for helpful discussions on the topic of this paper. We also thank the anonymous referees for helpful comments.

Cite As Get BibTex

Guillaume Sagnol and Daniel Schmidt genannt Waldschmidt. Restricted Adaptivity in Stochastic Scheduling. In 29th Annual European Symposium on Algorithms (ESA 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 204, pp. 79:1-79:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.ESA.2021.79

Abstract

We consider the stochastic scheduling problem of minimizing the expected makespan on m parallel identical machines. While the (adaptive) list scheduling policy achieves an approximation ratio of 2, any (non-adaptive) fixed assignment policy has performance guarantee Ω((log m)/(log log m)). Although the performance of the latter class of policies are worse, there are applications in which non-adaptive policies are desired. In this work, we introduce the two classes of δ-delay and τ-shift policies whose degree of adaptivity can be controlled by a parameter. We present a policy - belonging to both classes - which is an 𝒪(log log m)-approximation for reasonably bounded parameters. In other words, an exponential improvement on the performance of any fixed assignment policy can be achieved when allowing a small degree of adaptivity. Moreover, we provide a matching lower bound for any δ-delay and τ-shift policy when both parameters, respectively, are in the order of the expected makespan of an optimal non-anticipatory policy.

Subject Classification

ACM Subject Classification
  • Theory of computation → Scheduling algorithms
Keywords
  • stochastic scheduling
  • makespan minimzation
  • approximation algorithm
  • fixed assignment policy
  • non-anticipatory policy

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