NTP-INT: Network traffic prediction-driven in-band network telemetry for high-load switches

Abstract

Due to its real-time visibility, In-band network telemetry (INT) is of great significance for network management. Nevertheless, with the rapid growth of network devices and services, targeted access to detailed network information in dynamic environments has become increasingly essential. This paper proposes an intelligent network telemetry system called NTP-INT to obtain more fine-grained network information on high-load switches. Specifically, NTP-INT consists of three modules: the network traffic prediction module, the topology pruning module, and the probe path planning module. Firstly, the network traffic prediction module adopts a Multi-Temporal Graph Neural Network (MTGNN) to predict future network traffic and identify high-load switches. Then, we design the topology pruning algorithm to generate a subnetwork covering all high-load switches to reduce the complexity of probe path planning. Finally, the probe path planning module uses an attention-mechanism-based Deep Reinforcement Learning (DRL) model to plan efficient probe paths in the subnetwork. Experimental results demonstrate that NTP-INT achieves more accurate telemetry on high-load switches while reducing control overhead by 50%. Additionally, the topology pruning strategy shortens training time by over 40%.