SHIP: A Scalable High-performance IPv6 Lookup Algorithm that Exploits Prefix Characteristics

TL;DR

SHIP is a scalable IPv6 lookup algorithm that leverages prefix characteristics to achieve high performance, low latency, and efficient memory usage, suitable for future high-bandwidth network applications.

Contribution

The paper introduces SHIP, a novel IPv6 lookup algorithm that exploits prefix features to create a scalable, high-performance data structure optimized for low latency and memory efficiency.

Findings

SHIP reduces memory usage by at least 44% compared to existing approaches.

SHIP decreases memory latency by 61%.

The data structure scales logarithmically with the number of memory accesses.

Abstract

Due to the emergence of new network applications, current IP lookup engines must support high-bandwidth, low lookup latency and the ongoing growth of IPv6 networks. However, existing solutions are not designed to address jointly those three requirements. This paper introduces SHIP, an IPv6 lookup algorithm that exploits prefix characteristics to build a two-level data structure designed to meet future application requirements. Using both prefix length distribution and prefix density, SHIP first clusters prefixes into groups sharing similar characteristics, then it builds a hybrid trie-tree for each prefix group. The compact and scalable data structure built can be stored in on-chip low-latency memories, and allows the traversal process to be parallelized and pipelined at each level in order to support high packet bandwidth. Evaluated on real and synthetic prefix tables holding up to 580 k IPv6 prefixes, SHIP has a logarithmic scaling factor in terms of the number of memory accesses, and a linear memory consumption scaling. Using the largest synthetic prefix table, simulations show that compared to other well-known approaches, SHIP uses at least 44% less memory per prefix, while reducing the memory latency by 61%.