NTP-INT: Network Traffic Prediction-Driven In-band Network Telemetry for High-load Switches
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arXiv
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| Format: | Preprint |
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2025
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| author | Zhang, Penghui Zhang, Hua Dai, Yuqi Zeng, Cheng Wang, Jingyu Liao, Jianxin |
| author_facet | Zhang, Penghui Zhang, Hua Dai, Yuqi Zeng, Cheng Wang, Jingyu Liao, Jianxin |
| contents | 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\%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12834 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NTP-INT: Network Traffic Prediction-Driven In-band Network Telemetry for High-load Switches Zhang, Penghui Zhang, Hua Dai, Yuqi Zeng, Cheng Wang, Jingyu Liao, Jianxin Networking and Internet Architecture Machine Learning 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\%. |
| title | NTP-INT: Network Traffic Prediction-Driven In-band Network Telemetry for High-load Switches |
| topic | Networking and Internet Architecture Machine Learning |
| url | https://arxiv.org/abs/2502.12834 |