Data Generation and Optimization for Digital Twin Network Performance Modeling
draft-li-nmrg-dtn-data-generation-optimization-02
| Document | Type |
Expired Internet-Draft
(individual)
Expired & archived
|
|
|---|---|---|---|
| Authors | Mei Li , Cheng Zhou , Danyang Chen | ||
| Last updated | 2025-01-08 (Latest revision 2024-07-07) | ||
| 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This Internet-Draft is no longer active.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
Digital Twin Network (DTN) can be used as a secure and cost-effective environment for network operators to evaluate network performance in various what-if scenarios. Recently, AI models, especially neural networks, have been applied for DTN performance modeling. The quality of deep learning models mainly depends on two aspects: model architecture and data. This memo focuses on how to improve the model from the data perspective.
Authors
Mei Li
Cheng Zhou
Danyang Chen
(Note: The e-mail addresses provided for the authors of this Internet-Draft may no longer be valid.)