The advent of digital twins (DT) for the control and management of communication networks requires accurate and fast methods to estimate key performance indicators (KPI) needed for autonomous decision-making. Among several alternatives, queuing theory can be applied to model a real network as a queue system that propagates entities representing network traffic. By using fluid flow queue simulation and numerical methods, a good trade-off between accuracy and execution time can be obtained. In this work, we present the formal derivation and mathematical properties of a continuous fluid flow queuing model called the logistic queue model. We give novel proofs showing that this queue model has all the theoretical properties one should expect such as positivity of the queue and first-in first-out (FIFO) property. Moreover, extensions are presented in order to model different characteristics of telecommunication networks, including finite buffer sizes and propagation of flows with different priorities. Numerical results are presented to validate the accuracy and improved performance of our approach in contrast to traditional discrete event simulation, using synthetic traffic generated with the characteristics of real captured network traffic. Finally, we evaluate a DT built using a queue system based on the logistic queue model and demonstrate its applicability to estimate KPIs of an emulated real network under different traffic conditions.

翻译：用于通信网络控制与管理的数字孪生（DT）的出现，需要精确且快速的方法来估计自主决策所需的关键性能指标（KPI）。在多种备选方案中，排队论可用于将真实网络建模为一个传播代表网络流量实体的队列系统。通过采用流体流队列仿真和数值方法，可以在精度与执行时间之间获得良好的平衡。本文提出了一种称为物流队列模型的连续流体流排队模型的形式化推导与数学性质。我们给出了新的证明，表明该队列模型具备所有期望的理论性质，例如队列的正性以及先进先出（FIFO）特性。此外，本文还提出了若干扩展，以建模电信网络的不同特性，包括有限缓冲区大小以及不同优先级流量的传播。通过使用具有真实捕获网络流量特征的合成流量，我们展示了数值结果，以验证相较于传统离散事件仿真方法，本方法在精度和性能上的提升。最后，我们评估了一个基于物流队列模型构建的队列系统所实现的数字孪生，并论证了其在估计不同流量条件下仿真真实网络KPI的适用性。