Automatic Generation of Digital Twins for Network Testing
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908574683234304 |
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| author | Ding, Shenjia Flynn, David Harvey, Paul |
| author_facet | Ding, Shenjia Flynn, David Harvey, Paul |
| contents | The increased use of software in the operation and management of telecommunication networks has moved the industry one step closer to realizing autonomous network operation. One consequence of this shift is the significantly increased need for testing and validation before such software can be deployed. Complementing existing simulation or hardware-based approaches, digital twins present an environment to achieve this testing; however, they require significant time and human effort to configure and execute. This paper explores the automatic generation of digital twins to provide efficient and accurate validation tools, aligned to the ITU-T autonomous network architecture's experimentation subsystem. We present experimental results for an initial use case, demonstrating that the approach is feasible in automatically creating efficient digital twins with sufficient accuracy to be included as part of existing validation pipelines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03205 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic Generation of Digital Twins for Network Testing Ding, Shenjia Flynn, David Harvey, Paul Networking and Internet Architecture Machine Learning The increased use of software in the operation and management of telecommunication networks has moved the industry one step closer to realizing autonomous network operation. One consequence of this shift is the significantly increased need for testing and validation before such software can be deployed. Complementing existing simulation or hardware-based approaches, digital twins present an environment to achieve this testing; however, they require significant time and human effort to configure and execute. This paper explores the automatic generation of digital twins to provide efficient and accurate validation tools, aligned to the ITU-T autonomous network architecture's experimentation subsystem. We present experimental results for an initial use case, demonstrating that the approach is feasible in automatically creating efficient digital twins with sufficient accuracy to be included as part of existing validation pipelines. |
| title | Automatic Generation of Digital Twins for Network Testing |
| topic | Networking and Internet Architecture Machine Learning |
| url | https://arxiv.org/abs/2510.03205 |