Abstract
3Packet 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.
Original language | English |
---|---|
Pages (from-to) | 112384-112397 |
Number of pages | 14 |
Journal | IEEE Access |
Volume | 11 |
DOIs | |
State | Published - 2023 |
Keywords
- Machine learning
- P4
- packet classification
- programmable switches
- random forest