Statistical Optimal Hash-Based Longest Prefix Match

Abstract

Longest Prefix Match (LPM) is a basic and important function for current network devices. Hash-based approaches appear to be excellent candidate solutions for LPM with the capability of fast lookup speed and low latency. The number of hash table probes, i.e. the search path of a hash-based LPM algorithm, directly determines the lookup performance. In this paper, we propose Ω-LPM to improve the lookup performance by optimizing the search path of the hash-based LPM. Ω-LPM first reconstructs the forwarding table to support random search [19], then it applies a dynamic programming algorithm to find the shortest search path based on the statistics of the matching probabilities. Ω-LPM concretely reduces the number of hash table probes via searching most of the packets in optimal search paths. Even in the worst case, the upper bound of the average search path of Ω-LPM is 1 + log2(N), here N is the length of the longest prefix in the routing table. The case studies of the name lookup in Named Data Networking and the IP lookup in current Internet demonstrate that Ω-LPM can shorten 61.04% and 86.88% search paths compared with the basic hash-based methods of name lookup [22] and IP lookup [12], respectively, furthermore Ω-LPM reduces 32.3% probes of the name lookup and 73.55% probes of the IP lookup compared with the optimal linear search. The experimental results conducted on extensional name tables and IP tables also show that Ω-LPM has both low memory overhead and excellent scalability.