Enhancing Scalable Name-Based Forwarding
by Haowei Yuan, Patrick Crowley, and Tian Song.
ACM/IEEE Symposium Symposium on Architecture for Networking and Communications Systems (ANCS 2017), May 2017.
Name-based forwarding is a core component in informationcentric networks. Designing scalable name-based forwarding solutions is challenging because name prefixes are of variable length and the forwarding tables can be much longer than seen with IP. Recently, the speculative forwarding method has been proposed, in which the forwarding structure size is proportional to the information-theoretic differences between the name prefixes rather than their lengths. In this paper, our goal is to enhance name-based forwarding performance with memory- and time-efficient data structures. We first define the string differentiation problem, based on the behavior of speculative forwarding in core networks, and then propose fingerprint-based solutions for both triebased and hash table-based data structures. We experimentally demonstrate that the proposed solutions reduce the lookup latency and memory requirements. The proposed fingerprint-based Patricia trie decreases the average leaf-node depth and thus reduces the lookup latency. The proposed fingerprint-based hash table design requires only 3.2 GB of memory to store 1 billion names where each name has only one name component, and the measured lookup latency of the software-based single-threaded implementation is 0.29 microseconds. What’s more, the distributed forwarding scheme presented in this paper makes name-based forwarding truly scalable.