Offre en lien avec l’Action/le Réseau : – — –/– — –
Laboratoire/Entreprise : CNAM, CEDRIC
Durée : 3 years
Contact : elena.kornyshova@cnam.fr
Date limite de publication : 2020-12-18
Contexte :
Research laboratory: Computer Science and Communications department (CEDRIC; https://cedric.cnam.fr)
Research team: Networks and IoT Systems (ROC – Réseaux et Objets Connectés; https://roc.cnam.fr)
Related collaborative projects:
* H2020 AI@EDGE: European project on AI for beyond-5G networks, with 19 European partners.
* ANR INTELLIGENTSIA: national project on network automation with Orange, Inria, Acklio, Aguila.
Period: The 3-year contract would start on January 2021, but the beginning can be delayed by few months.
Salary: Appr. 24 000 € gross/year – 1 700 € net/month (before “prélèvement à la source”). In addition, 50% of the public transportation subscription can be reimbursed. Optional: teaching activities in French and/or English for up to 64 h/year, 2 650 €/year.
Sujet :
Intent-Based Networking (IBN) is a new paradigm arising in network management. It is driven by the possibility to leverage on network programming capabilities to implement service provisioning “intents”. An IBN solution meets “What to achieve” requirements expressed by users through User-to-Network Interfaces (UNIs); it is meant to support business goals and translate them into policies.
The interaction of an IBN system with a programmable infrastructure happens using Northbound Interfaces (NBIs) at the resource level, but can be the result of a composite intent translation chain from the UNI to many NBIs. For instance, taking the rising software-defined Radio Access Network environment (using ORAN for radio resource scheduling and ONAP for the orchestration layer), IBN can appear with orchestration intents at the ONAP user interface, and at the ORAN resource scheduling level at the near-real time controller, by means of the ORAN NBIs named A1 in the specifications; and as many NBI as resources (link, computing) can be solicited by the orchestration layer, so intents at the orchestration layer have to be deployable at the resource layers with resource-level intents. The standpoint of this project is therefore that the IBN-driven service orchestration is implemented across multiple resource-level NBIs, similarly to information system architectures.
Works at the state of the art declining the IBN framework to edge network infrastructure exist, such as [1] for vehicular applications; software-defined exchanges are defined therein as middleware for inter-layer IBN communications. A similar concept is used in [2,3] to consider the context for the IBN definition, touching several technical components. Techniques to identify and to process intents via context characteristics using artificial intelligence frameworks are studied in [1]. Nonetheless, a great confusion persists on the precise intent definition (different concepts are used to present intents: intentions, objectives, or else requirements) and its linkage with resource-level IBN configuration rules. For instance, major SDN controllers todays (e.g. ONOS, ODL) only very partially develop the IBN capabilities. Only in [2] context characteristics to define intents are clearly stated, such as traffic profile, required network function, device information. In this sense, AI and Machine Learning (ML) can help in defining methods to identify intentions and relevant context characteristics, to map them to an orchestration-level IBN process, then translated to resource-level IBN policies.
In this respect, the PhD project will address the following challenges:
– qualify the notion of intent in IBN, including its cross-layer implications in orchestration and resource-level systems, with a rigorous intent taxonomy that can be customized.
– define the context characteristics that should account by AI/ML processes or human-driven systems toward the definition of intents.
– design a cross-layer IBN framework with signaling requirements from UNI to NBI levels for expressing different network automation flavors (e.g., planning, real-time)
– experimentally show case the IBN signaling framework and its utility in network automation, using existing open networking software platforms.
References:
[1] A. Singh et al., “Intent-Based Network for Data Dissemination in Software-Defined Vehicular Edge Computing”. IEEE Transactions on Intelligent Transportation Systems, 1–9, 2020, early-access.
[2] D. Comer, A. Rastegatnia, “OSDF: An Intent-based Software Defined Network Programming Framework,” 2018 IEEE 43rd Conference on Local Computer Networks (LCN).
[3] J. Pan, McElhannon, “Future Edge Cloud and Edge Computing for Internet of Things Applications”. IEEE Internet of Things Journal 5 (1): 439–49, 2017.
Profil du candidat :
Master’s degree in computer science, computer engineering, or telecommunications engineering.
Formation et compétences requises :
Solid knowledge in Networks, IS, and Machine Learning
Adresse d’emploi :
2, rue Conté, Paris 75003