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Strategyproof Scheduling with Predictions

Authors Eric Balkanski , Vasilis Gkatzelis , Xizhi Tan

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ACM Subject Classification
  • Theory of computation → Algorithmic mechanism design
Keywords
  • Mechanism Design with Predictions
  • Strategyproof Scheduling

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Abstract

In their seminal paper that initiated the field of algorithmic mechanism design, Nisan and Ronen [Noam Nisan and Amir Ronen, 1999] studied the problem of designing strategyproof mechanisms for scheduling jobs on unrelated machines aiming to minimize the makespan. They provided a strategyproof mechanism that achieves an n-approximation and they made the bold conjecture that this is the best approximation achievable by any deterministic strategyproof scheduling mechanism. After more than two decades and several efforts, n remains the best known approximation and very recent work by Christodoulou et al. [George Christodoulou et al., 2021] has been able to prove an Ω(√n) approximation lower bound for all deterministic strategyproof mechanisms. This strong negative result, however, heavily depends on the fact that the performance of these mechanisms is evaluated using worst-case analysis. To overcome such overly pessimistic, and often uninformative, worst-case bounds, a surge of recent work has focused on the "learning-augmented framework", whose goal is to leverage machine-learned predictions to obtain improved approximations when these predictions are accurate (consistency), while also achieving near-optimal worst-case approximations even when the predictions are arbitrarily wrong (robustness).
In this work, we study the classic strategic scheduling problem of Nisan and Ronen [Noam Nisan and Amir Ronen, 1999] using the learning-augmented framework and give a deterministic polynomial-time strategyproof mechanism that is 6-consistent and 2n-robust. We thus achieve the "best of both worlds": an O(1) consistency and an O(n) robustness that asymptotically matches the best-known approximation. We then extend this result to provide more general worst-case approximation guarantees as a function of the prediction error. Finally, we complement our positive results by showing that any 1-consistent deterministic strategyproof mechanism has unbounded robustness.

Cite As Get BibTex

Eric Balkanski, Vasilis Gkatzelis, and Xizhi Tan. Strategyproof Scheduling with Predictions. In 14th Innovations in Theoretical Computer Science Conference (ITCS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 251, pp. 11:1-11:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.ITCS.2023.11

Author Details

Eric Balkanski
  • Columbia University, New York, NY, USA
Vasilis Gkatzelis
  • Drexel University, Philadelphia, PA, USA
Xizhi Tan
  • Drexel University, Philadelphia, PA, USA

Funding

All three authors were supported by NSF grant CCF-2210502
  • Balkanski, Eric: Columbia Center of AI Technology (CAIT), in collaboration with Amazon, faculty research award, and NSF grant IIS-2147361
  • Gkatzelis, Vasilis: NSF CCF-2047907, and NSF CCF-2008280
  • Tan, Xizhi: NSF CCF-2047907

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