PrefCache: Edge Cache Admission With User Preference Learning for Video Content Distribution

With the deployment of video streaming in 4G/5G mobile network, Content Delivery Networks (CDN) are extending to the network edge to provide end-users better Quality of Experience (QoE). However, small cache size and irregular request patterns make it a great challenge for edge caching in video content distribution. Most of the existing cache policies are item-wise, they admit each video object separately, which performs poorly on the network edge due to irregular request patterns. We observe that compared with single video objects, users' preferences for video topics are much more constant, thus are easier to be predicted. So we propose PrefCache, a novel cache admission policy based on preference learning, for video content edge caching. PrefCache enables an edge cache to learn users' preferences for videos in real-time. Once receiving a video object, PrefCache decides whether to admit it to the cache by whether it is under users' preference. We make three contributions in this work. (1) First, we design an information collector, which can proactively collect the preference-related information without any modification of clients and video providers. (2) Second, we propose a tree-structure model to learn and compress users' preferences. (3) Third, to decide which videos should be admitted to the cache in real-time, an explore-and-exploit method is applied. We carried out extensive experiments with 24 hours of trace data from a large commercial video content provider. The experimental results demonstrate that PrefCache can improve hit ratio up to 12%, and save 92% memory / 98% CPU overhead, compared to the state-of-the-art cache policies.