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Feasibility of Longest Prefix Matching using Learned Index Structures

Shunsuke Higuchi, Junji Takemasa, Yuki Koizumi, Atsushi Tagami, Toru Hasegawa

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.
Keywords: longest prefix matching / packet forwarding / forwarding information base

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