In-Network AR/CG Traffic Classification Entirely Deployed in the Programmable Data Plane: Unlocking RTP Features and L4S Integration

This paper presents an in-network machine learning (ML) approach for classifying Augmented Reality (AR) and Cloud Gaming (CG) traffic using programmable hardware. Random Forest (RF) models are deployed in a P4¹¹P4: Programming Protocol-independent Packet Processors data plane capable of processing Real-time Transport Protocol (RTP) traffic features like Frame Size (FS) and Inter-Frame Interval (IFI) for efficient classification. The classifier marks AR and CG traffic with Explicit Congestion Notification (ECN) codepoints to integrate with the Low Latency, Low Loss, Scalable Throughput (L4S) features of the programmable switch. The RF model prioritizes AR/CG traffic using Differentiated Services Code-Point (DSCP) assignments and modular ECN marking. The classification performance is evaluated using accuracy, precision, recall, and F1-score, while time overhead is assessed based on nodal processing time incurred during deployment by replaying AR/CG traffic. The P4 implementations for P4Pi²²https://eng.ox.ac.uk/computing/projects/programmable-hardware/p4pi.(V1Model) and Tofino Native Architecture (TNA) are all publicly available.