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This paper introduces a data-driven Network Digital Twin (NDT) framework, built on 6G-TWIN, to enable trustworthy what-if analysis for 6G cloud-edge infrastructures. The framework incorporates scalable cloud-edge telemetry aggregation, regime-aware feature engineering, and a validation methodology using Sign Agreement and Directional Sensitivity. Experiments on a Kubernetes cluster demonstrate that XGBoost-based NDTs achieve high regression accuracy (R2>0.99) and directional reliability (Sa>0.90) in extrapolating performance to unseen high-load regimes.
Network operators can now proactively scale resources with confidence, thanks to a digital twin that accurately predicts performance in previously unseen high-load scenarios.
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 (R2>0.99), while the XGBoost model delivers superior directional reliability (Sa>0.90), making the NDT a trustworthy tool for proactive resource scaling in out-of-distribution scenarios.