Scalable many-field packet classification using multidimensional-cutting via selective bit-concatenation

Abstract

OpenFlow Switch in Software-Defined Networking (SDN) has changed packet classification from standard 5-tuple to arbitrary many-field. The growing number of fields in a rule and the increasing number of rules in a ruleset poses great challenges for packet classification in terms of performance, storage, and update cost. In this paper, we design a two-stage packet classification system to address those issues by exploiting ruleset sparsity and rule fields independence. A ruleset is examined offline with proposed matrices to find representative bits from different field in a rule. We leverage those representative bits and concatenate them as sample values to divide a ruleset into several subsets in sample spaces. Each subset is given a unique address for each sample space. A ruleset update only affects those related addresses. The proposed pre-filtering stage comes out only highly related rules by intersecting candidate rules from different sample spaces for full match process. Out system throughput is 356 MPPS for 1K 15-field rules and 213 MPPS for 100K 15-field rules when using a single NVIDIA K20C GPU card.