An in-network caching scheme based on betweenness and content popularity prediction in content-centric networking

Content-centric Networking (CCN) is considered as a promising architecture to achieve reliable content distribution at large scale. One of the key research items of CCN is cache strategy, and most of the existing approaches consider little of the dynamicity of user interests. In this paper, we present a new cache policy, named as the betweenness and content popularity prediction (BEACON). Betweenness measures the importance of nodes in the whole network, and content popularity represents the user preference for service contents. By taking into account both network topology characteristics and flow distribution, the load of network and server is optimized. Moreover, we use the gray model to predict the content popularity, tracking the trend of user interest. The simulation results demonstrate that the BEACON scheme can effectively improve the cache hit rate, shorten access distance and reduce the delay of transmission.