Scalability of the Network Digital Twin in Complex Communication Networks

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.

Multimodal continual learning under constraints

Standard deep learning methods are designed to use static data. This induces a significant practical limitation when they are deployed in dynamic environments and are confronted with unknown data. Continuous learning provides a solution to this problem, especially with the use of large, pre-trained models. However, deploying such models in stand-alone mode is currently impossible in many frugal applications that impose heavy computational and/or memory constraints. Furthermore, most current methods are developed for a single modality (text or visual), whereas the data captured is often multimodal.
This thesis proposes to address several objectives that enable the practical deployment of agents capable of updating their representations under constraints. This deployment involves the following objectives:(1) the collection of domain-oriented corpora and their augmentation based on generative multimodal models, (2) the compression of foundation models to adapt them to the domain and make them usable under computational and/or memory constraints, (3) the proposal of efficient continuous learning methods to manage new multimodal data, and (4) the management of realistic data flows to take into account the specificities of different application contexts.

Generative artificial intelligence algorithms for understanding and countering online polarization

Digital platforms enable the widespread dissemination of information, but their engagement-centric business models often promote the spread of ideologically homogeneous or controversial political content. These models can lead to the polarization of political opinions and impede the healthy functioning of democratic systems. The PhD will investigate innovative generative AI models devised for a deep understanding of political polarization and for countering its effects. It will mobilize several areas of AI: generative learning, frugal AI, continual learning, and multimedia learning. Advances will be associated with the following challenges:
-the modeling of political polarization, and the translation of the obtained domain model into actionable implementation requirements that will be used as inputs of AI algorithms;
-the curation of massive and diversified multimodal political data to ensure topical and temporal coverage, and to map these data to a common semantic representation space;
-the training of politics-oriented generative models to encode relevant knowledge effectively and efficiently and to generate labeled training data for downstream tasks;
-the specialization of the models for the specific tasks needed for a fine-grained understanding of polarization (topic detection, entity recognition, sentiment analysis);
-the continual update of the politics-oriented generative models and polarization-specific tasks to keep pace with the evolution of political events and news.

High mobility mobile manipulator control in a dynamic context

The development of mobile manipulators capable of adapting to new conditions is a major step forward in the development of new means of production, whether for industrial or agricultural applications. Such technologies enable repetitive tasks to be carried out with precision and without the constraints of limited workspace. Nevertheless, the efficiency of such robots depends on their adaptation to the variability of the evolutionary context and the task to be performed. This thesis therefore proposes to design mechanisms for adapting the sensory-motor behaviors of this type of robot, in order to ensure that their actions are appropriate to the situation. It envisages extending the reconfiguration capabilities of perception and control approaches through the contribution of Artificial Intelligence, here understood in the sense of deep learning. The aim is to develop new decision-making architectures capable of optimizing robotic behaviors for mobile handling in changing contexts (notably indoor-outdoor), and for carrying out a range of precision tasks.

Learning Fine-Grained Dexterous Manipulation through Vision and Kinesthetic Observations

Fine-grained dexterous manipulation presents significant challenges for robots due to the need for precise object handling, coordination of contact forces, and utilization of visual observations. This research aims to address these challenges by investigating the integration of vision and kinesthetic sensors, sim2real techniques, and generalization through embodiment. The objective is to develop end-to-end algorithms and models that enable robots to manipulate objects with exceptional precision and adaptability. The research will focus on learning from large-scale data, transferring knowledge from simulations to real-world scenarios, and efficiently generalizing through low-shot fine-tuning.