An Intelligent Content Caching for NDN with Communication-Efficient State Sharing

Abstract

The built-in caching capability of Named Data Networking (NDN) is one of the most transformative proposals of next-generation network architecture, as it simultaneously realizes network traffic reduction, resilience to node failures, and prompt data retrieval. However, existing caching policies either make too many caches across the network by naively copying content everywhere or incur excess communication overhead for efficient caching through cache state exchange. Hence, we propose a machine learning-based caching policy along with a communication-efficient method to share the cache state. As the intelligent caching policy is capable of learning request patterns and estimating cache states of other nodes, our proposal realizes a more efficient utilization of the cache capacity by virtually considering the cache spaces of neighboring NDN nodes as an aggregated, larger cache space. The extensive evaluation experiments demonstrate the effectiveness of our proposal in increasing the cache hit ratio and reducing the average distance that each data travels to reach a requesting user. The effectiveness of the proposed cache state sharing method is empirically verified through an interpretable machine learning technique.