NTP-INT: Network Traffic Prediction-Driven In-band Network Telemetry for High-load Switches
This addresses network management challenges for operators by providing targeted telemetry, though it is incremental as it builds on existing in-band network telemetry methods.
The paper tackles the problem of obtaining fine-grained network information on high-load switches in dynamic network environments by proposing NTP-INT, an intelligent telemetry system that reduces control overhead by 50% while improving precision.
In-band network telemetry (INT) is essential to network management due to its real-time visibility. However, because of the rapid increase in network devices and services, it has become crucial to have targeted access to detailed network information in a dynamic network environment. 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: network traffic prediction module, network pruning module, and 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 network 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 (DEL) model to plan efficient probe paths in the network slice. The experimental results demonstrate that NTP-INT can acquire more precise network information on high-load switches while decreasing the control overhead by 50\%.