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 language | English (US) |
|---|---|
| Title of host publication | IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Volume | 2016-July |
| ISBN (Electronic) | 9781467399531 |
| DOIs | |
| State | Published - Jul 27 2016 |
| Event | 35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016 - San Francisco, United States Duration: Apr 10 2016 → Apr 14 2016 |
Other
| Other | 35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016 |
|---|---|
| Country | United States |
| City | San Francisco |
| Period | 4/10/16 → 4/14/16 |
ASJC Scopus subject areas
- Computer Science(all)
- Electrical and Electronic Engineering
Cite this
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 proceeding › Conference contribution
}
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 -