Feasibility of Longest Prefix Matching using Learned Index Structures

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.

Efficient Lookup Solutions for Named Data Networks

Named Data Networking (NDN) is afast growing architecture, which is proposed as an alternative to existing IP. NDN allows users to request the data identified by a unique name without any information of the hosting entity. NDN supports in-network caching of contents, multi-path forwarding, and data security. In NDN, packet-forwarding decisions are driven by lookup operations on content name of the NDN packets. An NDN node maintains set of routing tables that aid in forwarding decisions. Forwarding the NDN packets depend on lookup of these NDN tables and performing Longest Prefix Matching (LPM) against these NDN tables. The NDN names are unbounded and of variable length. These features along with large and dynamic NDN tables pose several challenges that include increased memory requirement and delayed lookup operations. To this end, there is a need for an efficient data structure that support fast lookup operations with low memory overhead. Several lookup techniques are proposed in this direction. Traversing trie structures would be slow since every level of trie require a memory access. Hash tables incur additional hash computations on names and suffer from collisions. Bloom filters suffer from false positives and do not support deletions. Improving the performance of these structures can lead to a better lookup solution.This survey paper explores different lookup structures for NDN networks. Performance is measured with respect to lookup rate and memory efficiency.