Content popularity and node level matched based probability caching for content centric networks

Content Centric Networking (CCN) is one of the candidate future Internet architectures that give favorable promises in networks. In-network caching as one of its key functions, has been studied extensively to achieve less content access delay and to reduce traffic load. However, current caching schemes mostly suffer from a poor utilization of cache space; since many content replicas are replaced without being hit by coming requests. As the limited cache space in networks, less popular contents have much less replicas than the popular ones, and the requests for them will cause considerable delay. In this paper, a content popularity and node level matched based probability caching strategy (MPC) is proposed to maximize the cache utilization and to improve the content diversity in networks simultaneously. We first evaluate the caching property of nodes along the delivery path by utilizing multi-parameters, and classify nodes into levels according to their caching property. Then, we assign contents to the nodes with their matched level, which balances the proportion of contents with different popularities in the network. Simulation results demonstrate that the proposed strategy can achieve less content access delay, higher cache hit ratio, and richer content diversity.