Network Digital Twins: A Key-Enabler for Zero-Touch Management in Industrial Communication Systems

Current industrial communication systems (ICS) are undergoing a transformation, leveraging a multitude of technologies to meet the specific needs of the manufacturing and automation industries. The convergence of these networks into edge, fog, and cloud architectures has enhanced their scalability and facilitated the deployment of advanced data-driven approaches, such as machine learning for optimizing production processes. However, ensuring proper provisioning of network and computation resources, along with delivering quality of service, is increasingly challenging in these complex communication systems. Zero-Touch Management (ZTM) frameworks promise to reduce complexity and minimize dependence on manual configuration by human experts. Successful deployment of such frameworks requires an accurate Network Digital Twin (NDT) of relevant network elements, as autonomous decision-making heavily relies on the quantity and quality of historical and real-time node and link state information provided by the NDT. However, the use of NDTs for ICS and ZTM in particular is still an emerging research topic. This paper therefore proposes a theoretical use-case for an NDT-based ZTM framework to improve resource utilization in cloud-centered networks. It presents a state-of-the-art analysis of recent NDT advances enabling the deployment of related ZTM approaches and discusses associated challenges and future research directions.