Document Open Access Logo

Better and Simpler Learning-Augmented Online Caching

Author Alexander Wei

PDF
Thumbnail PDF

File

LIPIcs.APPROX-RANDOM.2020.60.pdf
  • Filesize: 0.52 MB
  • 17 pages

Document Identifiers

Author Details

Alexander Wei
  • Harvard University, Cambridge, MA, USA

Acknowledgements

I would like to thank Jelani Nelson for advising this project and Bailey Flanigan for providing many helpful references.

Cite AsGet BibTex

Alexander Wei. Better and Simpler Learning-Augmented Online Caching. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 176, pp. 60:1-60:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2020.60

Abstract

Lykouris and Vassilvitskii (ICML 2018) introduce a model of online caching with machine-learned advice that marries the predictive power of machine learning with the robustness guarantees of competitive analysis. In this model, each page request is augmented with a prediction for when that page will next be requested. The goal is to design algorithms that (1) perform well when the predictions are accurate and (2) are robust in the sense of worst-case competitive analysis. We continue the study of algorithms for online caching with machine-learned advice, following the work of Lykouris and Vassilvitskii as well as Rohatgi (SODA 2020). Our main contribution is a substantially simpler algorithm that outperforms all existing approaches. This algorithm is a black-box combination of an algorithm that just naïvely follows the predictions with an optimal competitive algorithm for online caching. We further show that combining the naïve algorithm with LRU in a black-box manner is optimal among deterministic algorithms for this problem.

Subject Classification

ACM Subject Classification
  • Theory of computation → Caching and paging algorithms
  • Theory of computation → Machine learning theory
  • Computing methodologies → Machine learning
Keywords
  • Online caching
  • learning-augmented algorithms
  • beyond worst-case analysis

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Related Versions

References

  1. Dimitris Achlioptas, Marek Chrobak, and John Noga. Competitive analysis of randomized paging algorithms. Theor. Comput. Sci., 234(1-2):203-218, 2000. Google Scholar
  2. Antonios Antoniadis, Christian Coester, Marek Eliás, Adam Polak, and Bertrand Simon. Online metric algorithms with untrusted predictions. CoRR, abs/2003.02144, 2020. To appear at ICML 2020. URL: http://arxiv.org/abs/2003.02144.
  3. Nikhil Bansal, Niv Buchbinder, and Joseph Naor. A primal-dual randomized algorithm for weighted paging. J. ACM, 59(4):19:1-19:24, 2012. Google Scholar
  4. Nikhil Bansal, Niv Buchbinder, and Joseph Naor. Randomized competitive algorithms for generalized caching. SIAM J. Comput., 41(2):391-414, 2012. Google Scholar
  5. Avrim Blum and Carl Burch. On-line learning and the metrical task system problem. Mach. Learn., 39(1):35-58, 2000. Google Scholar
  6. Allan Borodin and Ran El-Yaniv. Online computation and competitive analysis. Cambridge University Press, 1998. Google Scholar
  7. Joan Boyar, Lene M. Favrholdt, Christian Kudahl, Kim S. Larsen, and Jesper W. Mikkelsen. Online algorithms with advice: A survey. ACM Comput. Surv., 50(2):19:1-19:34, 2017. Google Scholar
  8. Sébastien Bubeck and Aleksandrs Slivkins. The best of both worlds: Stochastic and adversarial bandits. In COLT 2012 - The 25th Annual Conference on Learning Theory, June 25-27, 2012, Edinburgh, Scotland, pages 42.1-42.23, 2012. Google Scholar
  9. Yihe Dong, Piotr Indyk, Ilya P. Razenshteyn, and Tal Wagner. Learning space partitions for nearest neighbor search. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, 2020. Google Scholar
  10. Amos Fiat, Richard M. Karp, Michael Luby, Lyle A. McGeoch, Daniel Dominic Sleator, and Neal E. Young. Competitive paging algorithms. J. Algorithms, 12(4):685-699, 1991. Google Scholar
  11. Sreenivas Gollapudi and Debmalya Panigrahi. Online algorithms for rent-or-buy with expert advice. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, pages 2319-2327, 2019. Google Scholar
  12. Chen-Yu Hsu, Piotr Indyk, Dina Katabi, and Ali Vakilian. Learning-based frequency estimation algorithms. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, 2019. Google Scholar
  13. Elias Koutsoupias and Christos H. Papadimitriou. Beyond competitive analysis. SIAM J. Comput., 30(1):300-317, 2000. Google Scholar
  14. Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, and Neoklis Polyzotis. The case for learned index structures. In Proceedings of the 2018 International Conference on Management of Data, SIGMOD Conference 2018, Houston, TX, USA, June 10-15, 2018, pages 489-504, 2018. Google Scholar
  15. Ravi Kumar, Manish Purohit, Aaron Schild, Zoya Svitkina, and Erik Vee. Semi-online bipartite matching. In 10th Innovations in Theoretical Computer Science Conference, ITCS 2019, January 10-12, 2019, San Diego, California, USA, pages 50:1-50:20, 2019. Google Scholar
  16. Ravi Kumar, Manish Purohit, Zoya Svitkina, and Erik Vee. Interleaved caching with access graphs. In Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, SODA 2020, Salt Lake City, UT, USA, January 5-8, 2020, pages 1846-1858, 2020. Google Scholar
  17. Silvio Lattanzi, Thomas Lavastida, Benjamin Moseley, and Sergei Vassilvitskii. Online scheduling via learned weights. In Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, SODA 2020, Salt Lake City, UT, USA, January 5-8, 2020, pages 1859-1877, 2020. Google Scholar
  18. Thodoris Lykouris and Sergei Vassilvitskii. Competitive caching with machine learned advice. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, pages 3302-3311, 2018. Google Scholar
  19. Mohammad Mahdian, Hamid Nazerzadeh, and Amin Saberi. Online optimization with uncertain information. ACM Trans. Algorithms, 8(1):2:1-2:29, 2012. Google Scholar
  20. Andres Muñoz Medina and Sergei Vassilvitskii. Revenue optimization with approximate bid predictions. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA, pages 1858-1866, 2017. Google Scholar
  21. Vahab S. Mirrokni, Shayan Oveis Gharan, and Morteza Zadimoghaddam. Simultaneous approximations for adversarial and stochastic online budgeted allocation. In Proceedings of the Twenty-Third Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2012, Kyoto, Japan, January 17-19, 2012, pages 1690-1701, 2012. Google Scholar
  22. Michael Mitzenmacher. A model for learned Bloom filters and optimizing by sandwiching. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada, pages 462-471, 2018. Google Scholar
  23. Michael Mitzenmacher. Scheduling with predictions and the price of misprediction. In 11th Innovations in Theoretical Computer Science Conference, ITCS 2020, January 12-14, 2020, Seattle, Washington, USA, pages 14:1-14:18, 2020. Google Scholar
  24. Manish Purohit, Zoya Svitkina, and Ravi Kumar. Improving online algorithms via ML predictions. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada, pages 9684-9693, 2018. Google Scholar
  25. Dhruv Rohatgi. Near-optimal bounds for online caching with machine learned advice. In Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, SODA 2020, Salt Lake City, UT, USA, January 5-8, 2020, pages 1834-1845, 2020. Google Scholar
  26. Tim Roughgarden. Beyond worst-case analysis. Commun. ACM, 62(3):88-96, 2019. Google Scholar
  27. Daniel Dominic Sleator and Robert Endre Tarjan. Amortized efficiency of list update and paging rules. Commun. ACM, 28(2):202-208, 1985. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact