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D EMANDE DE S TAGIAIRE
TUTEUR DE STAGE
Nom, Prénom Mathieu Bouet
Téléphone / E-mail 01 46 13 23 69 mathieu.bouet@thalesgroup.com
Direction DT/CEA/TAI
Numéro d’imputation du stagiaire Sous la forme de 2 fois 3 chiffres
DESCRIPTION DU STAGE
Durée / Dates souhaitées 6 mois
Lieu du Stage Gennevilliers
Famille professionnelle 04-Ingénierie Systèm
Présentation du service / Contexte Contenu (principales missions)
This project will be done in the Advanced Information Technologies (TAI) laboratory at Thales Communications & Security is involved in cutting-edge networking projects aiming at the specification, design and integration of security and telecommunication infrastructures. TAI personnel have expertise in several scientific fields: networking, especially for fixed and mobile IP network design, security architecture and information system. TAI is in charge of transferring technological building blocks to Thales’ business lines.
Middleboxes or network appliances like firewalls, proxies, ciphers and WAN optimizers have become an integral part of today's ISP and enterprise networks.
Middlebox functionalities are usually deployed on expensive and proprietary hardware that require trained personnel for deployment, configuration and maintenance. Moreover, the services provided by the network operator often require corresponding traffic to pass through a specific sequence of middleboxes for compliance with security and performance policies. This makes the middlebox deployment and maintenance tasks even more complicated.
Network Functions Virtualization (NFV) is an emerging and promising technology that is envisioned to overcome these challenges [1]. It proposes to move packet processing from dedicated hardware middleboxes to software running on commodity servers. In NFV terminology, software middleboxes are referred to as Virtualized Network Functions (VNFs). It is a challenging problem to determine the required number and placement of VNFs that optimizes network operational costs and utilization, without violating service level agreements. It is even more challenging to dynamically adapt the number of the VNFs and the routing of the flows to adapt to varying traffic and service requests. This problem is generally called the VNF chaining and orchestration problem.
INTITULE DU STAGE : Characterization of Virtual Network Functions chains in NFV
THALES COMMUNICATIONS &
SECURITY
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The objective of this project is to study different optimization algorithms that have been proposed in the literature to chain VNFs, for example [2,3,4], and characterize the nodes that tend to host the virtual network functions. To this aim, we will use classic centralities from the graph theory and we will explore very recent centralities based on the game theory, for example SV-based betweenness [5].
References:
[1] ETSI ISG NFV - Network Functions Virtualisation – White Paper #3:
https://portal.etsi.org/Portals/0/TBpages/NFV/Docs/NFV_White_Paper3.pdf [2] Md. Faizul Bari, Shihabur Rahman Chowdhury, Reaz Ahmed, Raouf Boutaba,
“On Orchestrating Virtual Network Functions in NFV”, http://arxiv.org/abs/1503.06377 , 2015.
[3] M. Bouet, J. Leguay and V. Conan, “Cost-based placement of vDPI functions in NFV infrastructures”, in Proceedings of the IEEE Network Softwarization Conference (NetSoft 2015), April 2015.
[4] M. C. Luizelli, L. R. Bays, L. Buriol, M. P. Barcellos, and L. P Gaspary, “Piecing together the NFV provisioning puzzle: Efficient placement and chaining of virtual network functions”, in IFIP/IEEE Integrated Network Management Symposium (IM 2015), May 2015.
[5] Piotr L. Szczepański, Tomasz Michalak, and Talal Rahwan, “A new approach to betweenness centrality based on the Shapley Value”, in Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1 (AAMAS '12), 2012.
PROFIL RECHERCHE Formation souhaitée
Ecoles ciblées
Ecole d'ingénieur Bac+5
Stage de fin d’études OUI NON
Compétences humaines et techniques : Outils, Langues, Logiciels …
Good knowledge on computer networks (routing, traffic engineering, graph theory etc.)
Good knowledge on operational research (Integer Linear Programming etc.)
Good programming skills (Python, Java, R, CPLEX, etc.)
