MultiSplit: An Efficient Algorithm for Packet Classification with Equivalent Priority

Abstract

Packet classification is a core function of network devices for providing advanced services, with the key challenge being to optimize classification speed while maintaining low memory usage. So far, many have proposed software-based packet classification solutions, with most of them adopting a multi-classifier architectures to accommodate the distribution of rule sets. Unfortunately, the need to perform lookups on each classifier during the packet classification stage significantly increases overhead, severely limiting classification speed. To address this shortfall, an efficient packet classification framework based on decision tree algorithms named MultiSplit is proposed. By leveraging the relationships of coverage and priority within the rule set, a new attribute can be abstracted for each rule, termed equivalent priority. Through this preprocessing, MultiSplit significantly reduces redundant lookup overhead while supporting the multi-classifier framework. Additionally, MultiSplit introduces a novel decision tree algorithm that combines multiple splits and intra-level binary search, significantly improving rule separation efficiency. The experimental results show that MultiSplit reduces memory consumption by 49% and decreases memory access by 63%, on average, compared with state-of-the-art packet classification algorithms.