Design and Evaluation of Probabilistic Caching in Information-Centric Networking.

In-network caching is a key feature of information-centric networking (ICN). However, challenges still exist in ICN caching such as how to place content replicas among cache nodes to maximize cache system benefits without introducing too much overhead. In this paper, we formulate the content placement problem and propose a distributed probabilistic caching strategy to enhance cache efficiency. Each node makes cache decision individually and caches passing content with certain probability, which is proportional to content popularity and content placement benefit. As a component of our scheme, we also propose an accurate method to predict the variations of content popularity. Besides, a global-popularity-based caching scheme is proposed to be used as a benchmark for performance evaluation. We conduct extensive simulations based on ndnSIM and evaluate our scheme on tree, intra-AS, and inter-AS topologies. Results indicate it outperforms the state-of-art schemes in terms of cache hit ratio, access latency, cache operation cost, and link bandwidth savings. It can achieve dramatic performance improvement, even in the case of a small cache size. In particular, the reduction of caching operation can reach up to two orders of magnitude. We also examine the impacts of various replacement policies on cache performance and perform overhead analysis. Finally, we give a simplified implementation of our scheme and validate it via simulations.