Adapting caching to audience retention rate
Adapting caching to audience retention rate
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根据受众保留率调整缓存
DOI：
10.1016/j.comcom.2017.11.015
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Jérémie Leguay
中科院分区：
文献类型：
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作者：
L. Maggi;Lazaros Gkatzikis;G. Paschos;Jérémie Leguay
用户很少完全观看在线内容。我们研究了如何考虑这一事实来提高视频点播和视频共享平台的缓存系统的性能，以减少核心网络上的流量。我们利用主流在线内容平台引入的“观众留存率”(ARR)的概念，衡量同一视频内容不同部分的受欢迎程度。当每个文件的流行度和ARR对高速缓存管理器可用时，我们首先表征能够存储视频文件的部分的高速缓存的性能限制。然后，我们放宽已知流行度的假设，并分析在每个文件的第一个区块上操作的自然适应最近最少使用(LRU)缓存替换策略的性能。我们称它为Chunk-LRU。我们证明了在较弱的内容热度分布假设下，选择较小的组块可以改善Chunk-LRU策略的性能，并且数值证明了即使对于少量的组块，Chunk-LRU的收益也几乎是最优的。最后，给出了实际系统中Chunk-LRU参数设计的指导原则。
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.