Scalable TCAM-based regular expression matching with compressed finite automata

Kun Huang (1) , Linxuan Ding (2) , Gaogang Xie (1) , Dafang Zhang (2) , Alex Liu (3) , Salamatian Kavé (4)

1 ICT - Institute of Computing Technology [Beijing]
2 Computer and Communication School
3 MSU CS - Computer Science and Engineering Dept.
4 LISTIC - Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance

Regular expression (RegEx) matching is a core function of deep packet inspection in modern network devices. Previous TCAM-based RegEx matching algorithms a priori assume that a deterministic finite automaton (DFA) can be built for a given set of RegEx patterns. However, practical RegEx patterns contain complex terms like wildcard closure and repeat character, and it may be impossible to build a DFA with a reasonable number of states. This results in prior work to being infeasible in practice. Moreover, TCAM-based RegEx matching is required to scale to a large-scale set of RegEx patterns. In this paper, we propose a compressed finite automaton implementation called (CFA) for scalable TCAM-based RegEx matching. CFA is designed to reduce TCAM space by using three compression techniques: transition, character, and state compressions. Experiments on realistic RegEx pattern sets show CFA highly outperforms previous solutions in terms of TCAM space, matching throughput, and TCAM power consumption.

Domaines: Réseaux et télécommunications [cs.NI]

Kun Huang, Linxuan Ding, Gaogang Xie, Dafang Zhang, Alex Liu, et al.. Scalable TCAM-based regular expression matching with compressed finite automata. Proceeding ANCS '13 Proceedings of the ninth ACM/IEEE symposium on Architectures for networking and communications systems, Oct 2013, San Jose, United States. pp.83-94. ⟨hal-00945208⟩