Feasibility of Longest Prefix Matching using Learned Index Structures

Abstract

This paper revisits longest prefix matching in IP packet forwarding because an emerging data structure, learned index, is recently presented. A learned index uses machine learning to associate key-value pairs in a key-value store. The fundamental idea to apply a learned index to an FIB is to simplify the complex longest prefix matching operation to a nearest address search operation. The size of the proposed FIB is less than half of an existing trie-based FIB while it achieves the computation speed nearly equal to the trie-based FIB. Moreover, the computation speed of the proposal is independent of the length of IP prefixes, unlike trie-based FIBs.

Towards instance-optimized data systems

“…The entire field of instance-optimized or learning-enhanced algorithms is evolving extremely rapidly across various communities. For example, the idea of the learned index alone has also been applied to other areas, including network package classification [99], DNA sequence search [46], longest prefix matches [44], and inverted indexes [90,114], and follow-on has appeared in database (e.g., VLDB [113]), systems (e.g., OSDI [111]), machine learning (e.g., NeurIPS [19]), networking (e.g., SIGCOMM [99]), and theory (e.g., Theor. Comput.…”