Popularity-based neighborhood collaborative caching for information-centric networks

Research on caching strategy is the key to improving network performance for Information-Centric Networks (ICNs). But it is still a great challenge to better utilize in-network caching of ICNs with low costs. In this paper, we propose a popularity-based neighborhood collaborative caching algorithm for ICNs. In the algorithm, in-network nodes track the popularity of contents, and a novel process is used for quick comparison of popularity in the algorithm. En-route and one-hop neighborhood nodes make caching decision collaboratively. Real-world topologies and different client placed scenes are used in the simulation experiments, and our algorithm performs better in terms of latency, cache hit ratio and path stretch compared with the state-of-the-art algorithms and ideal situations. The overhead and tradeoff of the algorithm on estimation of popularity and node interaction are also explored in details, and the proposed algorithm provides a practical choice for ICN caching decision strategy with its good performance and acceptable overhead.