Learning Forwarding Mechanism in Content-centric Networking

Abstract: In Content-centric Networking (CCN), data names are carried in packets without specifying IP addresses. This change leads to a new network forwarding model: CCN routers forward Interest (request) and Data packets using only their names. One major challenge is realizing intelligent forwarding of Interest packets over multiple available paths according to a Forwarding Information Base (FIB), which is currently configured manually with static routes. To address this issue, we propose a new FIB learning method by extending the current CCN prototype. With this new method, FIB entries can be automatically generated based on earlier successful data retrievals. We demonstrate that the proposed FIB learning forwarding mechanism is efficient and self-adaptive in adverse conditions such as link failure. This paper also illustrates an application example to demonstrate resilience when handling mobile nodes and effectiveness in changing network configurations