Poster: Prediction-Based Low-overhead In-band Network Telemetry

In-band network telemetry (INT) enables real-time and fine-grained network monitoring but incurs high transmission overhead. To mitigate this, encoding-based INT methods have been introduced to collect telemetry items using fewer bits than their original bits. However, prior works struggle to reduce encoding bit length when the magnitudes of telemetry items vary widely, as they rely on transmitting raw values. To address this challenge, we propose a prediction-based INT framework that effectively minimizes encoding bit length by collecting prediction errors instead of raw values. Our framework leverages both temporal patterns and inter-item correlations to robustly reduce prediction errors, thereby significantly lowering encoding bits while ensuring accurate reconstruction of original values.