Cache Behavior Characterization and Validation Over Large-Scale Video Data

Recent proliferation of mobile networks and smart devices drives the rapid growth of mobile video traffic. Caching popular video content at any possible place of the network near to users could significantly increase their delivery efficiency. However, fundamental problems of how cache behaves and what is the principle for cache deployment in a mobile network under large-scale video views are still unknown, which include three closely relevant problems: 1) what is the best scale of regions to deploy cache appliances; 2) how many contents should be cached; and 3) which contents should be cached. In this paper, we synthetically study these problems by analyzing 10 million video view requests of six most popular content providers, in the city of Shanghai, China. We first aggregate videos from different providers by topics to measure user interests, and divide the city into nonoverlapping regions of different sizes to investigate the influence of scale. Then, we define metrics of view concentration, popular topic number, cache revenue, and popular topic similarity to quantitatively characterize cache behaviors and consequently answer the three problems. Our studies reveal that: 1) it is effective to deploy cache in regions of a wide range of different scales; 2) the larger scale region and the regions with more views should cache more contents; and 3) different regions, especially small scale ones, should cache different contents. Furthermore, based on trace-driven evaluation, we show that the overall cache hit ratio can increase by up to 30% when we apply above guidelines for cache deployment.