AMSO-INT: Reinforcement Learning-Driven Dynamic Adaptive In-Band Telemetry

In-band Network Telemetry (INT) provides fine-grained, real-time insights into network data flows. A critical component of INT is the packet marking strategy, which determines which packets carry telemetry data---directly influencing the balance between visibility, overhead, and responsiveness. Traditional marking methods typically rely on fixed thresholds or manually tuned heuristics, limiting their adaptability in dynamic environments. In this paper, we introduce AMSO-INT (Adaptive Marking Strategy Optimizer for INT)---an approach of reinforcement learning (RL) implemented directly on the programmable data plane to optimize telemetry packet marking in real time. AMSO-INT maps key network indicators---such as link utilization gradient, queue occupancy, packet loss history, and INT header saturation---into a discrete state-action space and applies a tabular Q-learning to autonomously adjust marking intervals. By embedding decision logic within the data plane, AMSO-INT enables low-latency, closed-loop adaptation without control-plane intervention, a fundamentally novel approach in INT design. AMSO-INT effectively balances telemetry coverage, INT header bandwidth overhead, and anomaly detection latency. Experiments on a P4-based FatTree topology demonstrate that AMSO-INT achieves over 99.5% telemetry coverage while reducing bandwidth consumption by 21.3% compared to classic periodic strategies---highlighting its potential for scalable, intelligent, and self-optimizing network telemetry.