Graph Neural Network Based Modeling for Digital Twin Network
draft-wei-nmrg-gnn-based-dtn-modeling-00
| Document | Type |
Expired Internet-Draft
(individual)
Expired & archived
|
|
|---|---|---|---|
| Authors | Yong Cui , Wei Yunze , Zhiyong Xu , Peng Liu , Zongpeng Du | ||
| Last updated | 2023-10-14 (Latest revision 2023-04-12) | ||
| RFC stream | (None) | ||
| Intended RFC status | (None) | ||
| Formats | |||
| Stream | Stream state | (No stream defined) | |
| Consensus boilerplate | Unknown | ||
| RFC Editor Note | (None) | ||
| IESG | IESG state | Expired | |
| Telechat date | (None) | ||
| Responsible AD | (None) | ||
| Send notices to | (None) |
This Internet-Draft is no longer active. A copy of the expired Internet-Draft is available in these formats:
Abstract
This draft introduces the scenarios and requirements for performance modeling of digital twin networks, and explores the implementation methods of network models, proposing a network modeling method based on graph neural networks (GNNs). This method combines GNNs with graph sampling techniques to improve the expressiveness and granularity of the model. The model is generated through data training and validated with typical scenarios. The model performs well in predicting QoS metrics such as network latency, providing a reference option for network performance modeling methods.
Authors
Yong Cui
Wei Yunze
Zhiyong Xu
Peng Liu
Zongpeng Du
(Note: The e-mail addresses provided for the authors of this Internet-Draft may no longer be valid.)