Network digital twin: context, enabling technologies, and opportunities
Paul Almasan, Miquel Ferriol-Galmés, Jordi Paillissé, José Suárez‐Varela, Diego Perino, Diego López, Antonio Agustin Pastor Perales, Paul Harvey, Laurent Ciavaglia, Leon Wong, Shihan Xiao, Vishnu Ji Ram, Xiang Shi, Xiangle Cheng, Cabellos Aparicio, Alberto, Pere Barlet-Ros
Abstract
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.