Popularity-driven content caching

Suoheng Li, Jie Xu, Mihaela Van Der Schaar, Weiping Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

50 Citations (Scopus)

Abstract

This paper presents a novel cache replacement method - Popularity-Driven Content Caching (PopCaching). PopCaching learns the popularity of content and uses it to determine which content it should store and which it should evict from the cache. Popularity is learned in an online fashion, requires no training phase and hence, it is more responsive to continuously changing trends of content popularity. We prove that the learning regret of PopCaching (i.e., the gap between the hit rate achieved by PopCaching and that by the optimal caching policy with hindsight) is sublinear in the number of content requests. Therefore, PopCaching converges fast and asymptotically achieves the optimal cache hit rate. We further demonstrate the effectiveness of PopCaching by applying it to a movie.douban.com dataset that contains over 38 million requests. Our results show significant cache hit rate lift compared to existing algorithms, and the improvements can exceed 40% when the cache capacity is limited. In addition, PopCaching has low complexity.

Original languageEnglish (US)
Title of host publicationIEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2016-July
ISBN (Electronic)9781467399531
DOIs
StatePublished - Jul 27 2016
Event35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016 - San Francisco, United States
Duration: Apr 10 2016Apr 14 2016

Other

Other35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016
CountryUnited States
CitySan Francisco
Period4/10/164/14/16

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

Cite this

Li, S., Xu, J., Van Der Schaar, M., & Li, W. (2016). Popularity-driven content caching. In IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications (Vol. 2016-July). [7524381] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFOCOM.2016.7524381

Popularity-driven content caching. / Li, Suoheng; Xu, Jie; Van Der Schaar, Mihaela; Li, Weiping.

IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications. Vol. 2016-July Institute of Electrical and Electronics Engineers Inc., 2016. 7524381.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, S, Xu, J, Van Der Schaar, M & Li, W 2016, Popularity-driven content caching. in IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications. vol. 2016-July, 7524381, Institute of Electrical and Electronics Engineers Inc., 35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016, San Francisco, United States, 4/10/16. https://doi.org/10.1109/INFOCOM.2016.7524381
Li S, Xu J, Van Der Schaar M, Li W. Popularity-driven content caching. In IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications. Vol. 2016-July. Institute of Electrical and Electronics Engineers Inc. 2016. 7524381 https://doi.org/10.1109/INFOCOM.2016.7524381
Li, Suoheng ; Xu, Jie ; Van Der Schaar, Mihaela ; Li, Weiping. / Popularity-driven content caching. IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications. Vol. 2016-July Institute of Electrical and Electronics Engineers Inc., 2016.
@inproceedings{ab72c4c3341841b7b89ff99567f3cd82,
title = "Popularity-driven content caching",
abstract = "This paper presents a novel cache replacement method - Popularity-Driven Content Caching (PopCaching). PopCaching learns the popularity of content and uses it to determine which content it should store and which it should evict from the cache. Popularity is learned in an online fashion, requires no training phase and hence, it is more responsive to continuously changing trends of content popularity. We prove that the learning regret of PopCaching (i.e., the gap between the hit rate achieved by PopCaching and that by the optimal caching policy with hindsight) is sublinear in the number of content requests. Therefore, PopCaching converges fast and asymptotically achieves the optimal cache hit rate. We further demonstrate the effectiveness of PopCaching by applying it to a movie.douban.com dataset that contains over 38 million requests. Our results show significant cache hit rate lift compared to existing algorithms, and the improvements can exceed 40{\%} when the cache capacity is limited. In addition, PopCaching has low complexity.",
author = "Suoheng Li and Jie Xu and {Van Der Schaar}, Mihaela and Weiping Li",
year = "2016",
month = "7",
day = "27",
doi = "10.1109/INFOCOM.2016.7524381",
language = "English (US)",
volume = "2016-July",
booktitle = "IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Popularity-driven content caching

AU - Li, Suoheng

AU - Xu, Jie

AU - Van Der Schaar, Mihaela

AU - Li, Weiping

PY - 2016/7/27

Y1 - 2016/7/27

N2 - This paper presents a novel cache replacement method - Popularity-Driven Content Caching (PopCaching). PopCaching learns the popularity of content and uses it to determine which content it should store and which it should evict from the cache. Popularity is learned in an online fashion, requires no training phase and hence, it is more responsive to continuously changing trends of content popularity. We prove that the learning regret of PopCaching (i.e., the gap between the hit rate achieved by PopCaching and that by the optimal caching policy with hindsight) is sublinear in the number of content requests. Therefore, PopCaching converges fast and asymptotically achieves the optimal cache hit rate. We further demonstrate the effectiveness of PopCaching by applying it to a movie.douban.com dataset that contains over 38 million requests. Our results show significant cache hit rate lift compared to existing algorithms, and the improvements can exceed 40% when the cache capacity is limited. In addition, PopCaching has low complexity.

AB - This paper presents a novel cache replacement method - Popularity-Driven Content Caching (PopCaching). PopCaching learns the popularity of content and uses it to determine which content it should store and which it should evict from the cache. Popularity is learned in an online fashion, requires no training phase and hence, it is more responsive to continuously changing trends of content popularity. We prove that the learning regret of PopCaching (i.e., the gap between the hit rate achieved by PopCaching and that by the optimal caching policy with hindsight) is sublinear in the number of content requests. Therefore, PopCaching converges fast and asymptotically achieves the optimal cache hit rate. We further demonstrate the effectiveness of PopCaching by applying it to a movie.douban.com dataset that contains over 38 million requests. Our results show significant cache hit rate lift compared to existing algorithms, and the improvements can exceed 40% when the cache capacity is limited. In addition, PopCaching has low complexity.

UR - http://www.scopus.com/inward/record.url?scp=84983257869&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84983257869&partnerID=8YFLogxK

U2 - 10.1109/INFOCOM.2016.7524381

DO - 10.1109/INFOCOM.2016.7524381

M3 - Conference contribution

VL - 2016-July

BT - IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications

PB - Institute of Electrical and Electronics Engineers Inc.

ER -