Towards Trustworthy 6G Network Digital Twins: A Framework for Validating Counterfactual What-If Analysis in Edge Computing Resources

TL;DR

This paper introduces a scalable, data-driven framework for validating network digital twins in 6G edge computing, ensuring reliable what-if analysis for resource management under high load.

Contribution

It extends existing 6G-TWIN frameworks with scalable telemetry collection, regime-aware feature engineering, and a validation methodology for trustworthy performance extrapolation.

Findings

Deep Neural Networks and XGBoost achieve R2 > 0.99 in performance prediction.

XGBoost outperforms DNN in directional reliability with Sa > 0.90.

Framework successfully extrapolates to unseen high-load regimes.

Abstract

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.