Cuckoo filter-based many-field packet classification using X-tree

Abstract

Software-defined networking (SDN) is a new paradigm which emerged in the networking area. Packet classification is an interesting topic that has considered in both traditional and SDN networks. Packet classification involves inspection of multiple fields against a set of thousands of rules called rule-set. With the increasing throughput demands in modern networks and the growing size of rule-sets, performing wire-speed packet classification has become challenging and an important topic in recent years. Packet classification is called as many-field packet classification in the SDN because of increasing the number of header fields. In this paper, a scalable many-field packet classification by employing the extended tree (X-tree) integrated with an efficient probabilistic data structure called Cuckoo filter is proposed. X-tree has high performance from the lookup, insertion, and update aspects. However, X-tree has a high memory requirement, Cuckoo filter as a probabilistic data structure is integrated within each X-tree node to outperform memory requirements and providing more classification throughput. Our experiment results show that the proposed approach achieves high throughput while requiring low memory. In addition, the proposed approach improves latency 2.4 $$\times $$ , 6.15 $$\times $$ and 4.75 $$\times $$ in comparison with DBAMCP, BSOL-RC and BF-AQT for 64 k rule-set, respectively.