Probabilistic Caching Content Placement Method Based on Content-Centrality

A content-centrality-based probabilistic caching content placement method (CCPCP) is proposed to reduce cache redundancy as well as achieve better performance in terms of cache hits and utilization in information-centric networking (ICN). Different from those metrics that focus only on the centrality based on topology in the traditional network, the content centrality metric is developed in this paper. The content centrality metric not only describes the location centrality of cache nodes, but also describes the attribute of the content. In CCPCP method, each cache node individually makes a cache decision with a certain caching probability. In particular, each cache node adaptively calculates the caching probability by jointly considering the content centrality and the delay savings, which is proportional to the location centrality, the content popularity and the access delay savings. That is, the larger location centrality of cache node, the higher popularity of content, the more access delay savings, and the larger the caching probability of cache node caching the passing content. Simulation results show that CCPCP outperforms the state-of-art probabilistic methods in terms of cache hit ratio, caching content utilization ratio, access delay and cache redundancy under the less number of content replicas, even in the case that the reduction of number of content replicas is up to 32%.