Supporting Large Random Forests in the Pipelines of a Hardware Switch to Classify Packets at 100-Gbps Line Rate
Shie-Yuan Wang, Yinghua Wu · IEEE Access · 2023
Packet classification is an essential function for many applications such as QoS provisioning and network intrusion detection. In this work, we perform random forest classification in the pipelines of a hardware switch to classify packets at 100 Gbps line rate. We design, implement, and evaluate the performance of our scheme in a P4 (Programming Protocol-independent Packet Processors) programmable hardware switch. Experimental results show that our scheme can: (1) support a random forest composed of more than 100 decision trees in the pipelines of a hardware switch, and (2) use such a large random forest to classify packets at 100 Gbps line rate. In this paper, we design the match-action rules that are required to implement such a random forest in a P4 hardware switch. Besides, we analytically derive the formulas that give the number of these rules.