PPVC: online learning toward optimized video content caching

Today's Internet traffic has been dominated by video contents. To efficiently serve online videos for millions of users, it is essential to cache frequently requested videos on various devices such as edge servers, personal computers, etc. Existing caching algorithms are mainly designed by leveraging the information of past request records. However, it is insufficient to only use simple statistics of past request records, e.g., video popularity which ignores the occurrence time of past video request events and the discrepancies among individual devices. In this paper, we propose a radically different learning-based video caching algorithm for Internet devices, called PPVC (Point Process based Video Cache) which can take exact video request patterns into account. By utilizing Hawkes Process (HP), we are able to link the future video request rates of a device with three kinds of historical records, namely, historical requests for the same video launched by the device itself, historical requests for the same video from other devices with a similar request pattern, and historical requests for other similar videos from the same device. The video request patterns can be efficiently computed via Singular Value Decomposition (SVD). Parameters linking these historical events can be determined by maximizing the likelihood of historical events. To further reduce the computation load, we also propose an online version of PPVC, which can timely update cached videos with the incremental arrival of events. One nice property of PPVC is that it can adapt to the change of video popularity very fast and is also applicable to Internet devices at different levels, ranging from the high-level servers that serve a large population of users to the low-level user devices (e.g., personal computers). Finally, we conduct extensive simulations with real traces and the results show that PPVC can always achieve the best video caching performance in terms of the hit rate on all levels of Internet devices, and quickly adapt to the dynamics of video requests.