Online algorithms for weighted paging with predictions

Conference Paper

In this paper, 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 knowledge of the next request for every page is sufficient information for an algorithm to overcome existing lower bounds in weighted paging. However, a combination of the two, which we call the strong per request prediction (SPRP) model, 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.

Full Text

Duke Authors

Cited Authors

  • Jiang, Z; Panigrahi, D; Sun, K

Published Date

  • June 1, 2020

Published In

Volume / Issue

  • 168 /

International Standard Serial Number (ISSN)

  • 1868-8969

International Standard Book Number 13 (ISBN-13)

  • 9783959771382

Digital Object Identifier (DOI)

  • 10.4230/LIPIcs.ICALP.2020.69

Citation Source

  • Scopus