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.

Given the importance of robustness and resilience in emerging cloud-native networks, effective monitoring for fault detection is paramount and In-band network telemetry (INT) is a key candidate that enables real-time and fine-grained network monitoring with a programmable data plane. However, INT increases bandwidth overhead because network information is inserted directly into the packet header. In this paper, we propose a quantized INT (QINT) to effectively reduce overhead by considering the distribution of raw data. In QINT, the programmable switch encodes a raw telemetry item into a quantized bit stream using the Huffman coding scheme. To do this, QINT monitors the distribution of network telemetry items and encodes high-frequency data that are generated most of the time in a few bits. We implemented QINT on a programmable switch and our experimental results demonstrate that QINT can reduce the relative bandwidth usage by up to 60.6% compared to traditional INT, respectively.