Video Popularity Dynamics and its Implication for Replication

Popular online Video-on-Demand (VoD) services all maintain a large catalog of videos for their users to access. The knowledge of video popularity is very important for system operation, such as video caching on content distribution network (CDN) servers. The video popularity distribution at a given time is quite well understood. We study how the video popularity changes with time, for different types of videos, and apply the results to design video caching strategies. Our study is based on analyzing the video access levels over time, based on data provided by a large video service provider. Our main finding is, while there are variations, the glory days of a video’s popularity typically pass by quickly and the probability of replaying a video by the same user is low. The reason appears to be due to fairly regular number of users and view time per day for each user, and continuous arrival of new videos. All these facts will affect how video popularity changes, hence also affect the optimal video caching strategy. Based on the observation from our measurement study, we propose a mixed replication strategy (of LFU and FIFO) that can handle different kinds of videos. Offline strategy assuming tomorrow’s video popularity is known in advance is used as performance benchmark. Through trace driven simulation, we show that the caching performance achieved by the mixed strategy is very close to the performance achieved by the offline strategy.