TY - GEN
T1 - Popularity-driven content caching
AU - Li, Suoheng
AU - Xu, Jie
AU - Van Der Schaar, Mihaela
AU - Li, Weiping
N1 - Funding Information:
This research is supported by CSC. Weiping Li and Suoheng Li acknowledge the partial support from Intel Collaborative Research Institute for Mobile Networking and Computing. Jie Xu and Mihaela van der Schaar acknowledge the support of NSF CCF 1524417.
Publisher Copyright:
© 2016 IEEE.
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
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U2 - 10.1109/INFOCOM.2016.7524381
DO - 10.1109/INFOCOM.2016.7524381
M3 - Conference contribution
AN - SCOPUS:84983257869
T3 - Proceedings - IEEE INFOCOM
BT - IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016
Y2 - 10 April 2016 through 14 April 2016
ER -