A hybrid caching scheme based on manifold learning in CCN

Content-Centric Networking (CCN) proposals rethink the communication model around named data. In-network caching is a fundamental feature to distinguish the CCN from the current host-centric IP network. In this paper, we have proposed a hybrid caching scheme which combines the on-path one and the off-path one. We leverage the ISOMAP manifold learning algorithm to distinguish the importance of nodes. The important nodes apply LCE (Leave Copy Everywhere). So the future requests for popular content can be quickly satisfied by duplicate caching. The unimportant nodes apply a hash-routing-based cache strategy to cache the unpopular contents in order to increase the cache diversity. The proposed hybrid cache coordination scheme can balance between the user's QoE and resource utilization. Extensive experiments have been performed to evaluate the proposed scheme. Simulation results show that the proposed is effective and outperforms the existing caching mechanisms in CCN.