NTP-INT: Network Traffic Prediction-Driven In-band Network Telemetry for High-load Switches

TL;DR

NTP-INT is an intelligent in-band network telemetry system that uses traffic prediction, network pruning, and deep reinforcement learning to efficiently monitor high-load switches with reduced overhead.

Contribution

The paper introduces NTP-INT, a novel system combining traffic prediction, pruning, and deep RL for targeted, efficient network telemetry on high-load switches.

Findings

Achieves 50% reduction in control overhead.

Provides more precise network information on high-load switches.

Utilizes MTGNN and deep reinforcement learning for planning.

Abstract

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\%.