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.

翻译：网络数字孪生（NDT）可为6G云边基础设施提供安全的假设分析，但由于从遥测到验证的工作流程碎片化，其应用常受限制。我们提出一种数据驱动的NDT框架，通过可扩展管道实现云边遥测聚合与语义对齐，并将6G-TWIN扩展至统一数据模型。主要贡献包括：(i) 可扩展的云边遥测数据采集，(ii) 捕捉网络缩放行为的态势感知特征工程，(iii) 基于符号一致性与方向灵敏度的验证方法体系。在Kubernetes管理的集群上评估表明，该框架可外推至未知高负载场景的性能表现。深度神经网络（DNN）与XGBoost均实现高回归精度（R² > 0.99），其中XGBoost模型展现出更优的方向可靠性（Sa > 0.90），使NDT成为面向分布外场景的主动资源扩展的可信工具。