Cache Policy Based on Popularity Dynamics of YouTube Video Content

Abstract

In recent years, video traffic has rapidly increased, and reducing video traffic is an important issue for network providers. By caching video content at cache servers close to users, network providers can expect to reduce the video traffic in the networks. However, the storage capacity of cache servers is limited, so it is necessary to carefully select contents to be cached to effectively utilize the limited cache resources. In order to make effective use of cache resources, it is important to cache content based on the popularity dynamics of video contents. It is known that video contents have different popularity dynamics in each video category. For example, videos of movie and music categories tend to maintain view counts over long time, whereas the view counts of videos of news and sports categories rapidly decrease. In this paper, we propose a caching method that selects video content to be cached based on the popularity dynamics of video content in each category. To clarify the effectiveness of the proposed caching method, we evaluate the cache hit ratio of the proposed method by a trace-driven simulator using a measured request pattern of YouTube videos. We show that the proposed method improves the cache hit ratio compared with the LRU.