Affiliation:
1. Stanford University, CA, USA
2. Duke University, NC, USA
Abstract
In this article, we initiate the study of the weighted paging problem with predictions. This continues the recent line of work in online algorithms with predictions, particularly that of Lykouris and Vassilvitski (ICML 2018) and Rohatgi (SODA 2020) on unweighted paging with predictions. We show that unlike unweighted paging, neither a fixed lookahead nor a knowledge of the next request for every page is sufficient information for an algorithm to overcome the existing lower bounds in weighted paging. However, a combination of the two, which we call strong per request prediction (SPRP), suffices to give a 2-competitive algorithm. We also explore the question of gracefully degrading algorithms with increasing prediction error, and give both upper and lower bounds for a set of natural measures of prediction error.
Funder
NSF
NSF CAREER
Indo-US Virtual Networked Joint Center on Algorithms under Uncertainty
Publisher
Association for Computing Machinery (ACM)
Subject
Mathematics (miscellaneous)
Reference18 articles.
1. On the Influence of Lookahead in Competitive Paging Algorithms
2. Antonios Antoniadis, Christian Coester, Marek Eliás, Adam Polak, and Bertrand Simon. 2020. Online metric algorithms with untrusted predictions. In Proceedings of the 37th International Conference on Machine Learning, (ICML’20), 13–18 July 2020, Virtual Event (Proceedings of Machine Learning Research), Vol. 119. PMLR, 345–355. http://proceedings.mlr.press/v119/antoniadis20a.html.
3. A Primal-Dual Randomized Algorithm for Weighted Paging
4. A study of replacement algorithms for a virtual-storage computer
5. New Ressults on Server Problems
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