Route Lookup Algorithms Using the Novel Idea of Coded Prefix Trees
Mohammad Behdadfar, Hossein Saidi, Masoud Reza Hashemi
Abstract
Mohammad Behdadfar, Hossein Saidi, Masoud Reza Hashemi
Abstract
This paper introduces a new prefix matching algorithm called “Coded Prefix Search” and its improved version called “Scalar Prefix Search” using a coding concept for prefixes which can be implemented on a variety of trees especially limited height balanced trees for both IPv4 and IPv6 prefixes. Using this concept, each prefix is treated as a number. The main advantage of the proposed algorithms compared to Trie-based solutions is that the number of node accesses does not depend on IP address length in both search and update procedures. Therefore, applying this concept to balanced trees, causes the search and update node access complexities to be O(log n) where nis the number of prefixes. Also, compared to the existing range-based solutions, it does not need to store both end points of a prefix or to store ranges. Finally, compared to similar tree based solutions; it exhibits good storage requirements while it supports faster incremental updates. These properties make the algorithm capable of potential hardware implementation.
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This paper introduces a new prefix matching algorithm called “Coded Prefix Search” and its improved version called “Scalar Prefix Search” using a coding concept for prefixes which can be implemented on a variety of trees especially limited height balanced trees for both IPv4 and IPv6 prefixes. Using this concept, each prefix is treated as a number. The main advantage of the proposed algorithms compared to Trie-based solutions is that the number of node accesses does not depend on IP address length in both search and update procedures. Therefore, applying this concept to balanced trees, causes the search and update node access complexities to be O(log n) where nis the number of prefixes. Also, compared to the existing range-based solutions, it does not need to store both end points of a prefix or to store ranges. Finally, compared to similar tree based solutions; it exhibits good storage requirements while it supports faster incremental updates. These properties make the algorithm capable of potential hardware implementation.
Key concepts: Prefix, Trie, Computer science, Algorithm, Prefix code, Tree (set theory), Theoretical computer science, Node (physics)