Adapting caching to audience retention rate

Rarely do users watch online contents entirely. We study how to take this fact into account to improve the performance of cache systems for video-on-demand and video-sharing platforms, in terms of traffic reduction on the core network. We exploit the notion of “audience retention rate” (ARR), introduced by mainstream online content platforms and measuring the popularity of different parts of the same video content. We first characterize the performance limits of a cache able to store parts of video files, when the popularity and the ARR of each file are available to the cache manager. We then relax the assumption of known popularity and we analyze the performance of a natural adaptation of Least Recently Used (LRU) cache replacement policy that operates on the first chunks of each file. We call it chunk-LRU. We prove that, under a weak assumption on the content popularity distribution, choosing smaller chunks allows to improve the performance of chunk-LRU policy, and we show numerically that even for a small number of chunks, the gains of chunk-LRU are almost optimal. Finally, we provide some guiding principles for chunk-LRU parameter design in real systems.