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International audience; We consider a peer-to-peer electricity marketmodeled as a network game, where End Users (EUs) minimizetheir cost by computing their demand and generation whilesatisfying a set of local and coupling constraints. Their nominaldemand constitutes sensitive information, that they might wantto keep private. We prove that the network game admits aunique Variational Equilibrium, which depends on the privateinformation of all the EUs. A data aggregator is introduced,which aims to learn the EUs’ private information. The EUsmight have incentives to report biased and noisy readings topreserve their privacy, which creates shifts in their strategies.Relying on performative prediction, we define a decision-dependentgame G^stoch to couple the network game with adata market. Two variants of the Repeated Stochastic GradientMethod (RSGM) are proposed to compute the PerformativelyStable Equilibrium solution of G^stoch, that outperform RSGMwith respect to efficiency gap minimization, privacy preservation,and convergence rates in numerical simulations.

This thesis presents different novel resource allocation and pricing models for 5G network slicing. First, we devise a flexible sharing mechanism based on a bidding scheme which is provably overbooking-free even though the players’ bids are oblivious to infrastructure resources constraints. The proposed scheme can attain desirable fairness and efficiency figures to serve slice tenants and associated mobile users. This goal is attained by designing two coupled games entangled by the same Nash equilibrium. The first is a virtual game that generates the vector of prices of resources, and for which there exists a unique generalised Nash equilibrium. The Infrastructure Provider (InP) can use the price vector to drive the second game, a multidimensional Kelly mechanism based on the so-determined prices, where customers acquire a slice of resources at a price. We finally describe how to attain the Nash equilibrium of the game using an online procedure based on a primal-dual distributed algorithm. In the second work, we propose a flexible resource allocation and pricing scheme for slicing based on a combination of the Fisher market model and the Trading post mechanism. By properly pricing network resources, the desired allocation can be attained as a market equilibrium solution that not only maximizes network resource utilization but also assigns slice tenants with their favourite bundle. To make the scheme practically viable and enable tenants to reach market equilibrium in a decentralized manner, we devise budget distributing rules via trading post mechanisms that hand over tenants direct control to manage their preferences over resources under budget constraints. We theoretically evaluate the efficiency and fairness of the resulting allocations by comparing them with different baseline allocations.In our third endeavour, we study the business aspect of network slicing with a communication marketplace where slice tenants are in double-sided competition with each other. One competition is in terms of quality of service to attract the end-user to their services, and the second is to access the limited network resources for service provisioning. We model the competitive interaction between service providers (leaders) and end-users (followers) with the Stackelberg game, where end-users decide to choose their subscribers through an imitation process resulting in competition between the SPs as a multi-resource Tullock rent-seeking game. To determine resource pricing and allocation, we design two innovative market mechanisms. First, we assume that the Service providers (SPs) are pre-assigned with fixed infrastructure shares (budgets) and rely on a trading post mechanism to allocate the resource. Under this mechanism, the SPs can redistribute their budgets in bids and customise their allocations to maximise their profits. We investigate the strategic behaviour of SPs with a noncooperative game, which admits a unique Nash equilibrium when dealing with a single resource. Second, when SPs have no bound on their budget, we cast the problem as a coupled constraints game and show that the market prices can be obtained as the duals of the coupling constraints. Finally, we provide with different learning algorithms to compute solutions to the proposed market mechanisms., Cette thèse présente différents modèles novateurs d'allocation de ressources et de tarification pour le 5G network slicing. Dans un premier temps, nous élaborons un mécanisme de partage flexible basé sur un système d'enchères qui est démontré sans surréservation et ce même si les offres des joueurs ignorent les contraintes liées aux ressources. Le système proposé peut atteindre une équité et une efficacité adéquates permettant de répondre aux besoins des détenteurs de slices et des utilisateurs mobiles associés. Cet objectif est atteint par la création de deux jeux couplés qui sont liés par le même équilibre de Nash. Le premier est un jeu virtuel qui détermine le vecteur des prix des ressources, et pour lequel il existe un équilibre de Nash généralisé unique. Le fournisseur d'infrastructure peut ainsi utiliser le vecteur de prix pour piloter le deuxième jeu qui est un mécanisme de Kelly multidimensionnel basé sur les prix préalablement déterminés. Finalement, nous détaillons la méthode pour atteindre l'équilibre de Nash du jeu en utilisant un algorithme primal-dual distribué. Ensuite, nous proposons un schéma d'allocation des ressources et de tarification flexible pour le découpage en tranches reposant sur la combinaison du modèle de Fisher market et du mécanisme de trading post. En tarifant correctement les ressources du réseau, l'allocation recherchée peut être atteinte en tant que solution d'équilibre du marché qui non seulement maximise l'utilisation des ressources du réseau, mais aussi d'attribuer aux détenteurs de slices leur forfait (or paquet) de préférence. Afin de rendre le système pratique et de permettre aux détenteurs de slices d'atteindre l'équilibre du marché de façon décentralisée, nous concevons une règle de répartition des budgets via les mécanismes de trading post qui confèrent aux détenteurs de tranches un contrôle direct pour gérer leurs préférences sur les ressources sous contraintes budgétaires. Nous évaluons théoriquement l'efficacité et l'équité des allocations obtenues en les comparant à différentes allocations de référence .En dernier lieu, nous étudions l'aspect commercial du Network slicing dans le cadre d'un marché de la communication où les détenteurs de tranches, c'est-à-dire les fournisseurs de services, sont en double concurrence les uns avec les autres. Une concurrence se fait en termes de qualité de service pour inciter l'utilisateur final à utiliser leurs services, et la seconde est pour accéder aux ressources limitées du réseau pour la fourniture de services. Nous modélisons l'interaction concurrentielle entre les fournisseurs de services (leaders) et les utilisateurs finaux (suiveurs) par le jeu de Stackelberg où les utilisateurs finaux décident de choisir leurs abonnés par le biais d'un processus d'imitation, ce qui entraîne une concurrence entre les fournisseurs de services sous la forme d'un jeu Tullock rent-seeking multi-ressources. Pour déterminer la tarification et l'allocation des ressources, nous élaborons deux mécanismes de marché innovants. Premièrement, nous supposons que les fournisseurs de services se voient attribuer à l'avance des parts d'infrastructure fixes (budget) et utilisent un mécanisme de trading post pour allouer les ressources. Suivant ce mécanisme, les fournisseurs de services peuvent redistribuer leurs ressources dans les enchères et modifier leurs allocations pour maximiser leurs profits. Nous étudions le comportement stratégique des fournisseurs de services à l'aide d'un jeu non coopératif qui admet un équilibre de Nash unique lorsqu'il s'agit d'une seule ressource. Ensuite, lorsque les fournisseurs de services n'ont pas de limite budgétaire, nous considérons le problème comme un jeu à contraintes couplées et montrons ainsi que les prix du marché peuvent être obtenus en tant que duals des contraintes de couplage. Enfin, nous proposons trois algorithmes d'apprentissage différents destinés à déterminer les solutions aux mécanismes de marché proposés.

International audience; Network Slicing is one of the essential concepts that has been introduced in 5G networks design to support demand expressed by next generation services. Network slicing will also bring new business opportunities for service providers (SPs) and virtual network operators, allowing them to run their virtual, independent business operations on shared physical infrastructure. We consider a marketplace where service providers (SPs) i.e., slice tenants lease the resources from an infrastructure provider (InP) through a network slicing mechanism. They compete to offer a certain communication service to end-users. We show that the competition between SPs can be model using the multiresource Tullock contest (TC) framework, where SPs exert effort by expending costly resource to attract users. We study the competition between the SPs under a static and dynamic resource sharing scheme. In a dynamic resource sharing scheme, SPs are pre-assigned with fixed shares (budgets) of infrastructure, and they are allowed to redistribute their shares and customise their allocation to maximise their profit. The decision problem of SPs is analysed using non-cooperative game theory, and it is shown that the resultant game admits a unique Nash Equilibrium (NE). Furthermore, a distributed reinforcement algorithm is proposed that allows each SP to reach the game's unique Nash equilibrium. Finally, simulations results are conducted to analyse the interaction between market players and the economic efficacy of the network sharing mechanism.

In this paper, we present a resource allocation mechanism for network slices. We consider a dynamic resource allocation model with multiple independent resource providers. We call our allocation mechanism, the soft-max allocation mechanism, where the slices bid for resources and the resource providers allocate resources such that their revenue is close to the maximum revenue possible. We show that this mechanism translates into a game among the slices with a unique Nash equilibrium. We also show that the network utility obtained at this Nash equilibrium is close to the optimal social utility. We then present a stochastic dual sub-gradient algorithm that provably converges to the unique Nash equilibrium.

International audience; Slicing is emerging as a promising technique to support new differentiated services in 5G networks. It provides the necessary flexility and scalability associated with future services. To maintain satisfactory services requirements and high profit for service providers, a slice may be designing according to the varying demands and resource availability. This paper develops a framework for resources allocation between slicing and business layer for multi-tenant slicing, e.g. virtual wireless operators, service providers and smart cities services. This paper proposes a flexible mechanism based on a biding scheme for slicing allocation, which achieve desirable fairness and efficiency among the network slices of the different tenants and their associated users. We then design a practical algorithms to realise the proposed desired solution. We also show through the simulation the efficiency of our approach in term of efficiency and fairness.

International audience; Network slicing is becoming the platform of choice for several applications and services. Nowadays most applications are virtualized to gain flexibility and portability. With network slicing, operators can create multiple network slices or tenants, which can be used for certain applications with specific requirements. Behind the network slicing, a slice expresses the need to access a precise service type, under a fully qualified set of computing and network requirements. Resource allocation decision encompasses a combination of different resource types (e.g., radio resource, CPU, memory, bandwidth). In this paper, we explore a differential pricing scheme that maximizes social welfare among slices as well as among end-users. To do so, we propose a pricing mechanism that makes fairness at multiple levels: fairness among slices and fairness among slice locations supported by each slice. Therefore, the proposed scheme is beneficial for both the slices and the end-users independent of their location. Additionally, we study the case where slices can manipulate their preferences to improve their utility. We show that the Fisher market game always has a pure Nash equilibrium and we prove Price of Anarchy is 1 N , where N is the number of slices. Finally, we conduct simulations using Amazon EC2 instances to numerically analyze and compare the performance of the mechanisms and confirm the theoretical properties of the market model.