Towards a Robust Transport Network With Self-adaptive Network Digital Twin
🤖 AI Summary
To address synchronization inaccuracy and insufficient resilience of Network Digital Twins (NDTs) under abrupt traffic fluctuations, this paper proposes an adaptive NDT architecture focused on dynamic runtime maintenance. A lightweight telemetry module continuously collects real-time traffic data, while a concept drift detection mechanism triggers automatic retraining and updating of the Virtual Twin (VTwin) model, enabling closed-loop synchronization between the virtual twin and the physical network. The key innovation lies in tightly coupling concept drift identification with the twin’s evolutionary process, thereby significantly improving synchronization timeliness and environmental adaptability. Experimental evaluations across diverse network topologies and traffic patterns demonstrate that, following traffic concept drift, the proposed method achieves an average 56.7% improvement in prediction accuracy over baseline approaches lacking synchronization mechanisms, while also exhibiting superior robustness and predictive fidelity.
Application Category
📝 Abstract
The ability of the network digital twin (NDT) to remain aware of changes in its physical counterpart, known as the physical twin (PTwin), is a fundamental condition to enable timely synchronization, also referred to as twinning. In this way, considering a transport network, a key requirement is to handle unexpected traffic variability and dynamically adapt to maintain optimal performance in the associated virtual model, known as the virtual twin (VTwin). In this context, we propose a self-adaptive implementation of a novel NDT architecture designed to provide accurate delay predictions, even under fluctuating traffic conditions. This architecture addresses an essential challenge, underexplored in the literature: improving the resilience of data-driven NDT platforms against traffic variability and improving synchronization between the VTwin and its physical counterpart. Therefore, the contributions of this article rely on NDT lifecycle by focusing on the operational phase, where telemetry modules are used to monitor incoming traffic, and concept drift detection techniques guide retraining decisions aimed at updating and redeploying the VTwin when necessary. We validate our architecture with a network management use case, across various emulated network topologies, and diverse traffic patterns to demonstrate its effectiveness in preserving acceptable performance and predicting per-flow delay under unexpected traffic variation. The results in all tested topologies, using the normalized mean square error as the evaluation metric, demonstrate that our proposed architecture, after a traffic concept drift, achieves a performance improvement in prediction of at least 56.7% compared to a configuration without NDT synchronization.
Problem
Research questions and friction points this paper is trying to address.
Enhancing synchronization between virtual and physical transport network twins
Improving resilience of NDT platforms against traffic variability
Providing accurate delay predictions under fluctuating traffic conditions
Innovation
Methods, ideas, or system contributions that make the work stand out.
Self-adaptive NDT architecture for delay predictions
Telemetry and drift detection for VTwin synchronization
Resilient data-driven NDT against traffic variability
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