Agudelo J.J., Soto P., Zaki-Hindi A., Sottet J.S., Faye S., Slamnik-Kriještorac N., Marquez-Barja J., Botero M.C.
2026 Joint European Conference on Networks and Communications and 6g Summit Eucnc 6g Summit 2026, pp. 504-511, 2026
Network Digital Twins (NDTs) enable safe what-if analysis for 6G cloud-edge infrastructures, but adoption is often limited by fragmented workflows from telemetry to validation. We present a data-driven NDT framework that extends 6G-TWIN with a scalable pipeline for cloud-edge telemetry aggregation and semantic alignment into unified data models. Our contributions include: (i) scalable cloud-edge telemetry collection, (ii) regime-aware feature engineering capturing the network's scaling behavior, and (iii) a validation methodology based on Sign Agreement and Directional Sensitivity. Evaluated on a Kubernetes-managed cluster, the framework extrapolates performance to unseen high-load regimes. Results show both Deep Neural Network (DNN) and XGBoost achieve high regression accuracy (R<sup>2</sup>>0.99), while the XGBoost model delivers superior directional reliability (S<sub>a</sub>>0.90), making the NDT a trustworthy tool for proactive resource scaling in out-of-distribution scenarios.
