The proliferation of emergent network applications (e.g., telesurgery, metaverse) is increasing the difficulty of managing modern communication networks. These applications entail stringent network requirements (e.g., ultra-low deterministic latency), which hinders network operators to manage their resources efficiently. In this article, we introduce the network digital twin (NDT), a renovated concept of classical network modeling tools whose goal is to build accurate data-driven network models that can operate in real-time. We describe the general architecture of the NDT and argue that modern machine learning (ML) technologies enable building some of its core components. Then, we present a case study that leverages a ML-based NDT for network performance evaluation and apply it to routing optimization in a QoS-aware use case. Lastly, we describe some key open challenges and research opportunities yet to be explored to achieve effective deployment of NDTs in real-world networks.
翻译:新兴网络应用(如远程外科、逆向)的激增正在增加管理现代通信网络的困难,这些应用涉及严格的网络要求(如超低确定性拉长),妨碍网络运营商有效管理其资源。在本条中,我们引入了网络数字双(NDT),这是一个经过更新的经典网络模型工具概念,目的是建立准确的数据驱动网络模型,可以实时运行。我们描述了NDT的总体结构,认为现代机器学习(ML)技术有助于建设其核心组成部分。然后,我们提出了一个案例研究,利用基于ML的NDT进行网络性能评估,并在Qos-aware使用案例中应用它来优化路线。最后,我们描述了一些有待探索的关键开放的挑战和研究机会,以便在现实世界网络中有效部署NDTs。