A comprehensive survey of Network Digital Twin architecture, capabilities, challenges, and requirements for Edge–Cloud Continuum

Network Digital Twin (NDT) collects data from physical, virtual, and software components and supports real-time network performance analysis, emulation, and intelligent physical network control. This paper surveys the current state of NDT specifications and explores NDT benefits for Network Operators (NOs) and its possible roles in future network management. It discusses the NDT key components, architecture, and integration of Machine Learning and Artificial Intelligence models in the NDT. Further, it covers virtualization technology management, suitability of Software-Defined Networking capabilities, and simulation tools to empower NDT. Two perspectives make the position of this survey different from existing studies; first, it highlights NDT limitations regarding Edge–Cloud Continuum (ECC) contextualization. ECC is a purposeful trending integration of Edge and Cloud Computing, involving multiple stakeholders like Service Providers, Customers, and Platform or Infrastructure Providers. However, current NDT specifications have not mentioned the ways to benefit stakeholders other than NOs. We also discuss notable computing and communication technologies transformations necessary to consider during NDT modeling, the existing data models, and reusable vocabularies that can be extended to achieve a detailed ECC representation for all stakeholders, essentially for Service Providers and Customers. Secondly, a data model is proposed that covers descriptive and prescriptive features and aims to provide a granular representation of ECC components to meet stakeholders’ requirements and render particular user information views. Different explored NDT perspectives, and proposed data model reduces the impact of existing NDT limitations in ECC representation.