DBTable: Leveraging Discriminative Bitsets for High-Performance Packet Classification

Abstract

Packet classification, as a crucial function of networks, has been extensively investigated. In recent years, the rapid advancement of software-defined networking (SDN) has introduced new demands for packet classification, particularly in supporting dynamic rule updates and fast lookup. This paper presents a novel structure called DBTable for efficient packet classification to achieve high overall performance. DBTable integrates the strengths of conventional packet classification methods and neural network concepts. Within DBTable, a straightforward indexing scheme is proposed to eliminate rule replication, thereby ensuring high update performance. Additionally, we propose an iterative method for generating a discriminative bitset (DBS) to evenly partition rules. By utilizing the DBS, rules can be efficiently mapped in a hash table, thus achieving exceptional lookup performance. Moreover, DBTable incorporates a hybrid structure to further optimize the worst-case lookup performance, primarily caused by data skewness. The experiment results on 12 256k rulesets show that, compared to seven state-of-the-art schemes, DBTable achieves an overall lookup speed improvement ranging from 1.53x to 7.29x, while maintaining the fastest update speed.