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

arXiv: https://arxiv.org/abs/2502.12834 · HTML: https://mlsanity.com/paper/2502.12834/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [Reinforcement learning](https://mlsanity.com/paper/2405.10369/) (2024)
- [Deep Reinforcement Learning for Multi-objective Optimization](https://mlsanity.com/paper/1906.02386/) (2019)
- [PINT: Probabilistic In-band Network Telemetry](https://mlsanity.com/paper/2007.03731/) (2020)
- [007: Democratically Finding The Cause of Packet Drops](https://mlsanity.com/paper/1802.07222/) (2018)
- [Micro-burst in Data Centers: Observations, Implications, and Applications](https://mlsanity.com/paper/1604.07621/) (2016)
- [AdapINT: A Flexible and Adaptive In-Band Network Telemetry System Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2310.19331/) (2023)

## Abstract (arXiv)

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%.