Many-Field Packet Classification for Software-Defined Networking Switches

Abstract

Packet classification is a core problem for OpenFlow-based software-defined networking switches, which required 38 packet header fields per flow to be examined against thousands of rules in a ruleset. With the trend of continue growing number of fields in a rule and the number of rules in rule set, it will be a great challenge to design a high performance packet classification solution with the capability to easy update new rule and fields. In this paper, we present a scalable many-field packet classification algorithm with varying rulesets and its prototype implementation on a graphics processing unit. The proposed algorithm constructs multiple lookup tables and merges partial lookup results for a small ruleset to accelerate the overall packet classification process by using effective bit positions in a ruleset with three selecting metrics: wildcard ratio, independence index, and diversity index. Those lookup tables made with effective bit positions are flat with a low rule replication ratio. Besides, they are adjustable to meet different implementation environments for a good performance scalability between different ruleset sizes. Our prototype on a single NVIDIA K20C GPU achieves 198 MPPS, 186 MPPS, 163 MPPS throughput for 1K, 32K, and 100K 15-field ruleset.