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Section: Bilateral Contracts and Grants with Industry

Bilateral Contracts with Industry

Neo members are involved in the

  • Inria-Nokia Bell Labs joint laboratory: the joint laboratory consists of five ADRs (Action de Recherche/Research Action) in its third phase (starting October 2017). Neo members participate in one ADR “Distributed Learning and Control for Network Analysis” (see §8.1.1).

  • Inria-Qwant joint laboratory “Smart search is privacy” (see §8.1.2);

  • Inria-Orange Labs joint laboratory (see §8.1.3).

ADR Nokia on the topic “Distributed Learning and Control for Network Analysis” (October 2017 – September 2021)

Participants : Eitan Altman, Konstantin Avrachenkov, Mandar Datar, Maximilien Dreveton, Alain Jean-Marie.

Over the last few years, research in computer science has shifted focus to machine learning methods for the analysis of increasingly large amounts of user data. As the research community has sought to optimize the methods for sparse data and high-dimensional data, more recently new problems have emerged, particularly from a networking perspective that had remained in the periphery.

The technical program of this ADR consists of three parts: Distributed machine learning, Multiobjective optimisation as a lexicographic problem, and Use cases / Applications. We address the challenges related to the first part by developing distributed optimization tools that reduce communication overhead, improve the rate of convergence and are scalable. Graph-theoretic tools including spectral analysis, graph partitioning and clustering will be developed. Further, stochastic approximation methods and D-iterations or their combinations will be applied in designing fast online unsupervised, supervised and semi-supervised learning methods.

Qwant contract on “Asynchronous on-line computation of centrality measures” (15 December 2017 – 14 May 2020)

Participants : Nicolas Allegra, Konstantin Avrachenkov.

  • Contractor: Qwant

  • Collaborator: Sylvain Peyronnet

We shall study asynchronously distributed methods for network centrality computation. The asynchronous distributed methods are very useful because they allow efficient and flexible use of computational resources on the one hand (e.g., using a cluster or a cloud) and on the other hand they allow quick local update of centrality measures without the need to recompute them from scratch.

Orange CIFRE on the topic “Self-organizing features in the virtual 5G radio access network” (November 2017 – October 2020)

Participants : Eitan Altman, Marie Masson.

The considerable extent of the complexity of 5G networks and their operation is in contrast with the increasing demands in terms of simplicity and efficiency. This antagonism highlights the critical importance of network management. Self-Organizing Networks (SON), which cover self-configuration, self-optimization and self-repair, play a central role for 5G Radio Access Network (RAN).

This CIFRE thesis aims at innovating in the field of managing 5G RAN, with a special focus on the features of the SON-5G. Three objectives are identified: a) develop self-organizing features (SON in 5G-RAN), b) develop cognitive managing mechanisms for the SON-5G features developed, and c) demonstrate how do the self-organizing mechanisms fit in the virtual RAN (vRAN).

Huawei CIFRE on the topic “Scalable Online Algorithms for SDN controllers” (June 2016 – May 2019)

Participants : Zaid Allybokus, Konstantin Avrachenkov.

Software-Defined Networking (SDN) technologies have radically transformed network architectures. They provide programmable data planes that can be configured from a remote controller platform.

The objective of this CIFRE thesis is to provide fundamental answers on how powerful SDN controller platforms could solve large online flow problems to optimize networks in real-time and in a distributed or semi-distributed fashion. We use methods from both optimization and dynamic programming.



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