Orchestration of Pollution/qos-aware Control - Nancy, France - Centre de Recherche en Automatique de Nancy ( CRAN )

Description

Orchestration of pollution/QoS-aware control strategies for SD-IIoT:

• Réf
  ABG-120458
• Sujet de Thèse 19/02/2024
• Contrat doctoral
• Centre de Recherche en Automatique de Nancy ( CRAN )
• Lieu de travail
• Nancy
• Grand Est
• France
• Intitulé du sujet
• Orchestration of pollution/QoSaware control strategies for SD-IIoT
• Champs scientifiques
• Informatique
• Sciences de l'ingénieur
• Télécommunications

• Mots clés

• Control of networks, IoT networks, SDN architecture, graph theory
  Description du sujet:

In recent years, the footprint of digital communication networks on the environment and society has
emerged as a major issue in the deployment of communication infrastructures (we are aiming at
environmentally aware, or even sober networks - a term used by the legislator, but without a real
definition). This aspect should not be reduced to the sole minimization of local energy consumption for
a single communication, but on the contrary to the whole infrastructure (of end-to-end services in a
potentially multi-actor and multi-technology context) and to other metrics including the different
sources of pollution (for example, the carbon cost per bit with the consideration of the mode of
production of the energy used or the radio-frequency pollution).
Moreover, the networks of the future (especially 5/6G), the pillars of digital ubiquity, must be able to
reconfigure themselves automatically, whether to support a new management strategy in the context
of the industry of the future or the provision of specific services during a temporary event such as a
sporting event. This is even more the case in industrial and wireless Internet of Things environments,
where the dynamics of traffic, mobility, QoS requirements (such as range or bandwidth) and
environment are massive.
This topic is at the confluence of these two themes, where it becomes necessary to implement
network control architectures (usually centralized Software/Intelligent-Defined Networking). Such
strategies must then optimize a budget shared by the whole network, concatenating both pollution and
QoS metrics. The scientific state of the art for such integrative strategies is still limited (either to traffic
evolution or to energy consumption optimization only), but first works at CRAN have highlighted their
In the context of a more sober architecture, the strategies lead to the partitioning of sub-networks, to
the reduction of capacities, and potentially to their putting on standby/shutdown. It is then necessary
to ensure that the translation of a strategy into an ordered set of rules for each equipment does not
generate inconsistencies in the data plane (need to define a migration order) and that the network
controller can recover the control of its equipment (how to access a part of the network that would
have been disconnected?). The problematic of this thesis covers more the ability to implement the
optimal solution computed by the controller. The literature is relatively incomplete here since it
essentially addresses the stability of the controller and the cost of reconfiguration, but not its implementation on the architecture. This problem is even more obvious when the communication
strategy between the controller and the devices is in-band, i.e. when it uses data transport links (and
not dedicated links), which reinforces the need to ensure a priori the durability of a communication
channel between the controller(s) and the devices. More generally, the issue of scaling and the
complexity of the selected algorithms remains a point to be evaluated.
Therefore, this thesis aims to orchestrate the different reconfiguration instructions of an IIoT
architecture to reduce its environmental footprint. It is structured as follows. The first step concerns
the state of the art of network control solutions in IoT (especially based on 5/6G protocols) and those
processes in place in current SDN controllers to translate an infrastructure control strategy into a set
of rules. In the second step, a learning strategy to optimize the budget of an IoT infrastructure will be
defined and will be used as a reference for the rest of the work (the control plan could be based on
routing, the management of slices, or on the control of the transmission power).
The associated keywords will thus concern network metrology (e.g. energy), learning (e.g. channels),
and reactivity/reconfigurability. Then, we will analyze the impact of the topology (/graph) structure on
the ability to implement a given strategy. For a given structure, is it necessary to use a centralized,
decentralized, or distributed (multi-controller) architecture? Which controller placement is optimal?
Which links (and associated configurations) should be kept? Are there any constraints on the
formation of clusters? The answers to these questions will allow us to define a network reconfiguration
strategy (potentially based on intermediate data plans) that does not jeopardize its stability and its
fut