Trend-Aware Video Caching Through Online Learning

This paper presents Trend-Caching, a novel cache replacement method that optimizes cache performance according to the trends of video content. Trend-Caching explicitly learns the popularity trend of video content and uses it to determine which video it should store and which it should evict from the cache. Popularity is learned in an online fashion and requires no training phase, hence it is more responsive to continuously changing trends of videos. We prove that the learning regret of Trend-Caching (i.e., the gap between the hit rate achieved by Trend-Caching and that by the optimal caching policy with hindsight) is sublinear in the number of video requests, thereby guaranteeing both fast convergence and asymptotically optimal cache hit rate. We further validate the effectiveness of Trend-Caching 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. Furthermore, Trend-Caching has low complexity.