Cookies?
Library Header Image
LSE Research Online LSE Library Services

Efficient caching with reserves via marking

Ibrahimpur, Sharat, Purohit, Manish, Svitkina, Zoya, Vee, Erik and Wang, Joshua R. (2023) Efficient caching with reserves via marking. In: Etessami, Kousha, Feige, Uriel and Puppis, Gabriele, (eds.) 50th International Colloquium on Automata, Languages, and Programming, ICALP 2023. Leibniz International Proceedings in Informatics, LIPIcs. Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. ISBN 9783959772785

[img] Text (Efficient Caching with Reserves via Marking) - Published Version
Available under License Creative Commons Attribution.

Download (911kB)

Identification Number: 10.4230/LIPIcs.ICALP.2023.80

Abstract

Online caching is among the most fundamental and well-studied problems in the area of online algorithms. Innovative algorithmic ideas and analysis – including potential functions and primal-dual techniques – give insight into this still-growing area. Here, we introduce a new analysis technique that first uses a potential function to upper bound the cost of an online algorithm and then pairs that with a new dual-fitting strategy to lower bound the cost of an offline optimal algorithm. We apply these techniques to the Caching with Reserves problem recently introduced by Ibrahimpur et al. [10] and give an O(log k)-competitive fractional online algorithm via a marking strategy, where k denotes the size of the cache. We also design a new online rounding algorithm that runs in polynomial time to obtain an O(log k)-competitive randomized integral algorithm. Additionally, we provide a new, simple proof for randomized marking for the classical unweighted paging problem.

Item Type: Book Section
Additional Information: © 2023 The Author(s)
Divisions: Mathematics
Subjects: Q Science > QA Mathematics
Date Deposited: 17 Aug 2023 10:15
Last Modified: 20 Dec 2024 00:19
URI: http://eprints.lse.ac.uk/id/eprint/120004

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics