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Strategyproof Scheduling with Predictions

Authors Eric Balkanski , Vasilis Gkatzelis , Xizhi Tan

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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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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

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.

Subject Classification

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

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References

  1. Priyank Agrawal, Eric Balkanski, Vasilis Gkatzelis, Tingting Ou, and Xizhi Tan. Learning-augmented mechanism design: Leveraging predictions for facility location. In David M. Pennock, Ilya Segal, and Sven Seuken, editors, EC '22: The 23rd ACM Conference on Economics and Computation, Boulder, CO, USA, July 11 - 15, 2022, pages 497-528. ACM, 2022. Google Scholar
  2. Antonios Antoniadis, Themis Gouleakis, Pieter Kleer, and Pavel Kolev. Secretary and online matching problems with machine learned advice. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, pages 7933-7944, 2020. Google Scholar
  3. Itai Ashlagi, Shahar Dobzinski, and Ron Lavi. Optimal lower bounds for anonymous scheduling mechanisms. Math. Oper. Res., 37(2):244-258, 2012. Google Scholar
  4. Yossi Azar, Debmalya Panigrahi, and Noam Touitou. Online graph algorithms with predictions. Proceedings of the Thirty-Third Annual ACM-SIAM Symposium on Discrete Algorithms, 2022. Google Scholar
  5. Eric Balkanski, Tingting Ou, Clifford Stein, and Hao-Ting Wei. Scheduling with speed predictions. CoRR, abs/2205.01247, 2022. URL: http://arxiv.org/abs/2205.01247.
  6. Étienne Bamas, Andreas Maggiori, Lars Rohwedder, and Ola Svensson. Learning augmented energy minimization via speed scaling. In Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020. Google Scholar
  7. Etienne Bamas, Andreas Maggiori, and Ola Svensson. The primal-dual method for learning augmented algorithms. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, pages 20083-20094, 2020. Google Scholar
  8. Evripidis Bampis, Konstantinos Dogeas, Alexander V. Kononov, Giorgio Lucarelli, and Fanny Pascual. Scheduling with untrusted predictions. In Luc De Raedt, editor, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, pages 4581-4587. ijcai.org, 2022. Google Scholar
  9. Siddhartha Banerjee, Vasilis Gkatzelis, Artur Gorokh, and Billy Jin. Online nash social welfare maximization with predictions. In Proceedings of the 2022 ACM-SIAM Symposium on Discrete Algorithms, SODA 2022. SIAM, 2022. Google Scholar
  10. George Christodoulou, Elias Koutsoupias, and Annamária Kovács. Mechanism design for fractional scheduling on unrelated machines. ACM Trans. Algorithms, 6(2):38:1-38:18, 2010. Google Scholar
  11. George Christodoulou, Elias Koutsoupias, and Annamária Kovács. On the nisan-ronen conjecture. In 62nd IEEE Annual Symposium on Foundations of Computer Science, FOCS 2021, Denver, CO, USA, February 7-10, 2022, pages 839-850. IEEE, 2021. Google Scholar
  12. George Christodoulou, Elias Koutsoupias, and Angelina Vidali. A lower bound for scheduling mechanisms. In Nikhil Bansal, Kirk Pruhs, and Clifford Stein, editors, Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, New Orleans, Louisiana, USA, January 7-9, 2007, 2007. Google Scholar
  13. George Christodoulou, Elias Koutsoupias, and Angelina Vidali. A lower bound for scheduling mechanisms. Algorithmica, 55(4):729-740, 2009. Google Scholar
  14. Shahar Dobzinski and Ariel Shaulker. Improved lower bounds for truthful scheduling. CoRR, abs/2007.04362, 2020. URL: http://arxiv.org/abs/2007.04362.
  15. Paul Dütting, Silvio Lattanzi, Renato Paes Leme, and Sergei Vassilvitskii. Secretaries with advice. In Proceedings of the 22nd ACM Conference on Economics and Computation, pages 409-429, 2021. Google Scholar
  16. Yiannis Giannakopoulos, Alexander Hammerl, and Diogo Poças. A new lower bound for deterministic truthful scheduling. In Tobias Harks and Max Klimm, editors, Algorithmic Game Theory - 13th International Symposium, SAGT 2020, Augsburg, Germany, September 16-18, 2020, Proceedings, volume 12283 of Lecture Notes in Computer Science, pages 226-240. Springer, 2020. Google Scholar
  17. Vasilis Gkatzelis, Kostas Kollias, Alkmini Sgouritsa, and Xizhi Tan. Improved price of anarchy via predictions. In David M. Pennock, Ilya Segal, and Sven Seuken, editors, EC '22: The 23rd ACM Conference on Economics and Computation, Boulder, CO, USA, July 11 - 15, 2022, pages 529-557. ACM, 2022. Google Scholar
  18. Sungjin Im, Ravi Kumar, Mahshid Montazer Qaem, and Manish Purohit. Online knapsack with frequency predictions. Advances in Neural Information Processing Systems, 34, 2021. Google Scholar
  19. Elias Koutsoupias and Angelina Vidali. A lower bound of 1+phi for truthful scheduling mechanisms. In Ludek Kucera and Antonín Kucera, editors, Mathematical Foundations of Computer Science 2007, 32nd International Symposium, MFCS 2007, Ceský Krumlov, Czech Republic, August 26-31, 2007, Proceedings, volume 4708 of Lecture Notes in Computer Science, pages 454-464. Springer, 2007. Google Scholar
  20. Silvio Lattanzi, Thomas Lavastida, Benjamin Moseley, and Sergei Vassilvitskii. Online scheduling via learned weights. In Shuchi Chawla, editor, Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, SODA 2020, Salt Lake City, UT, USA, January 5-8, 2020, pages 1859-1877. SIAM, 2020. Google Scholar
  21. Jan Karel Lenstra, David B. Shmoys, and Éva Tardos. Approximation algorithms for scheduling unrelated parallel machines. Math. Program., 46:259-271, 1990. Google Scholar
  22. Shi Li and Jiayi Xian. Online unrelated machine load balancing with predictions revisited. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 6523-6532. PMLR, 2021. Google Scholar
  23. Alexander Lindermayr and Nicole Megow. Alps. URL: https://algorithms-with-predictions.github.io/.
  24. Thodoris Lykouris and Sergei Vassilvtiskii. Competitive caching with machine learned advice. In International Conference on Machine Learning, pages 3296-3305. PMLR, 2018. Google Scholar
  25. Michael Mitzenmacher. Scheduling with predictions and the price of misprediction. In Thomas Vidick, editor, 11th Innovations in Theoretical Computer Science Conference, ITCS 2020, January 12-14, 2020, Seattle, Washington, USA, volume 151 of LIPIcs, pages 14:1-14:18. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020. Google Scholar
  26. Michael Mitzenmacher and Sergei Vassilvitskii. Algorithms with Predictions, pages 646-662. Cambridge University Press, 2021. URL: https://doi.org/10.1017/9781108637435.037.
  27. Noam Nisan and Amir Ronen. Algorithmic mechanism design (extended abstract). In Jeffrey Scott Vitter, Lawrence L. Larmore, and Frank Thomson Leighton, editors, Proceedings of the Thirty-First Annual ACM Symposium on Theory of Computing, May 1-4, 1999, Atlanta, Georgia, USA, pages 129-140. ACM, 1999. Google Scholar
  28. Noam Nisan and Amir Ronen. Algorithmic mechanism design. Games Econ. Behav., 35(1-2):166-196, 2001. Google Scholar
  29. Manish Purohit, Zoya Svitkina, and Ravi Kumar. Improving online algorithms via ml predictions. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems. Curran Associates, Inc., 2018. Google Scholar
  30. Manish Purohit, Zoya Svitkina, and Ravi Kumar. Improving online algorithms via ML predictions. In Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, and Roman Garnett, editors, Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, pages 9684-9693, 2018. Google Scholar
  31. Michael E. Saks and Lan Yu. Weak monotonicity suffices for truthfulness on convex domains. In John Riedl, Michael J. Kearns, and Michael K. Reiter, editors, Proceedings 6th ACM Conference on Electronic Commerce (EC-2005), Vancouver, BC, Canada, June 5-8, 2005, pages 286-293. ACM, 2005. Google Scholar
  32. Chenyang Xu and Pinyan Lu. Mechanism design with predictions. In Luc De Raedt, editor, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, pages 571-577. ijcai.org, 2022. URL: https://doi.org/10.24963/ijcai.2022/81.
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