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Title: Online Algorithms for Weighted Paging with Predictions
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.  more » « less
Award ID(s):
1955703
PAR ID:
10405294
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
ACM Transactions on Algorithms
Volume:
18
Issue:
4
ISSN:
1549-6325
Page Range / eLocation ID:
1 to 27
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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