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 quality of service (QoS) metrics under unexpected traffic variation, such as delay and jitter. 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 per-flow delay and jitter prediction of at least 64% and 21%, respectively, compared to a configuration without NDT synchronization.

翻译：网络数字孪生（NDT）保持对其物理对应物（称为物理孪生（PTwin））变化的感知能力，是实现及时同步（亦称为孪生化）的基本条件。因此，在传输网络场景中，一个关键需求是处理意外的流量变化，并动态调整以在关联的虚拟模型（称为虚拟孪生（VTwin））中维持最优性能。在此背景下，我们提出了一种新型NDT架构的自适应实现方案，该方案旨在即使在波动的流量条件下也能提供精确的时延预测。该架构解决了一个文献中尚未充分探讨的核心挑战：提升数据驱动NDT平台对流量变化的韧性，并改善VTwin与其物理对应物之间的同步性。因此，本文的贡献立足于NDT生命周期，聚焦于运行阶段——该阶段利用遥测模块监测输入流量，并采用概念漂移检测技术来指导旨在必要时更新和重新部署VTwin的再训练决策。我们通过网络管理用例，在多种仿真网络拓扑和多样化流量模式下验证了该架构，以证明其在意外流量变化（如时延和抖动）下保持可接受性能及预测服务质量（QoS）指标的有效性。在所有测试拓扑中，使用归一化均方误差作为评估指标的结果表明，在发生流量概念漂移后，我们提出的架构在每流时延和抖动预测方面，相比未进行NDT同步的配置，分别实现了至少64%和21%的性能提升。