Communication networks are experiencing an exponential growth both in terms of deployment of network infrastructures (particularly observed in the gradual and sustained evolution towards 6G networks), but also in terms of machines, covering a wide range of devices ranging from Cloud servers to lightweight embedded IoT components (e.g. System on Chip: SoC), and including mobile terminals such as smartphones.

This ecosystem also encompasses a variety of software components ranging from applications (e.g. A/V streaming) to the protocols from different communication network layers. Furthermore, such an ecosystem is intrinsically dynamic because of the following features:
- Change in network topology: due, for example, to hardware/software failures, user mobility, operator network resource management policies, etc.
- Change in the usage/consumption ratio of network resources (bandwidth, memory, CPU, battery, etc.). This is due to user needs and operator network resource management policies, etc.

To ensure effective supervision or management, whether fine-grained or with an abstract view, of communication networks, various network management services/platforms, such as SNMP, CMIP, LWM2M, CoMI, SDN, have been proposed and documented in the networking literature and standard bodies. Furthermore, the adoption of such management platforms has seen broad acceptance and utilization within the network operators, service providers, and the industry, where the said management platforms often incorporate advanced features, including automated control loops (e.g. rule-based, expert-system-based, ML-based), further enhancing their capability to optimize the performance of the network management operations.

Despite the extensive exploration and exploitation of these network management platforms, they do not guarantee an effective (re)configuration without intrinsic risks/errors, which can cause serious outage to network applications and services. This is particularly true when the objective of the network (re)configuration is to ensure real-time optimization of the network, analysis/ tests in operational mode (what- if analysis), planning updates/modernizations/extensions of the communication network, etc. For such (re)configuration objectives, a new network management paradigm has to be designed.

In the recent years, the communication network research community started exploring the adoption of the digital twin concept for the networking context (Network Digital Twin: NDT). The objective behind this adoption is to help for the management of the communication network for various purposes, including those mentioned in the previous paragraph.

The NDT is a digital twin of the real/physical communication network (Physical Twin Network: PTN), making it possible to manipulate a digital copy of the real communication network, without risk. This allow in particular for visualizing/predicting the evolution (or the behavior, the state) of the real network, if this or that network configuration is to be applied. Beyond this aspect, the NDT and the PTN network exchange information via one or more communication interfaces with the aim of maintaining synchronized states between the NDT and the PTN.

Nonetheless, setting up a network digital twin (NDT) is not a simple task. Indeed, frequent and real-time PTN-NDT synchronization poses a scalability problem when dealing with complex networks, where each network information is likely to be reported at the NDT level (e.g. a very large number of network entities, very dynamic topologies, large volume of information per node/per network link).

Various scientific contributions have attempted to address the question of the network digital twin (NDT). The state-of-the-art contributions focus on establishing scenarios, requirements, and architecture for the NDT. Nevertheless, the literature does not tackle the scalability problem of the NDT.

The objective of this PhD thesis is to address the scalability problem of network digital twins by exploring new machine learning models for network information selection and prediction.