The effect of competition among brokers on the quality and price of differentiated internet services

Price war, as an important factor in undercutting competitors and attracting customers, has spurred considerable work that analyzes such conflict situation. However, in most of these studies, quality of service (QoS), as an important decision-making criterion, has been neglected. Furthermore, with the rise of service-oriented architectures, where players may offer different levels of QoS for different prices, more studies are needed to examine the interaction among players within the service hierarchy. In this paper, we present a new approach to modeling price competition in (virtualized) service-oriented architectures, where there are multiple service levels. In our model, brokers, as the intermediaries between end-users and service providers, offer different QoS by adapting the service that they obtain from lower-level providers so as to match the demands of their clients to the services of providers. To maximize profit, players, i.e. providers and brokers, at each level compete in a Bertrand game while they offer different QoS. To maintain an oligopoly market, we then describe underlying dynamics which lead to a Bertrand game with price constraints at the providers' level. Numerical simulations demonstrate the behavior of brokers and providers and the effect of price competition on their market shares.This work has been partly supported by National Science Foundation awards: CNS-0963974, CNS-1346688, CNS-1536090 and CNS-1647084

Multi-attribute demand characterization and layered service pricing

As cloud computing gains popularity, understanding the pattern and structure of its workload is increasingly important in order to drive effective resource allocation and pricing decisions. In the cloud model, virtual machines (VMs), each consisting of a bundle of computing resources, are presented to users for purchase. Thus, the cloud context requires multi-attribute models of demand. While most of the available studies have focused on one specific attribute of a virtual request such as CPU or memory, to the best of our knowledge there is no work on the joint distribution of resource usage. In the first part of this dissertation, we develop a joint distribution model that captures the relationship among multiple resources by fitting the marginal distribution of each resource type as well as the non-linear structure of their correlation via a copula distribution. We validate our models using a public data set of Google data center usage. Constructing the demand model is essential for provisioning revenue-optimal configuration for VMs or quality of service (QoS) offered by a provider. In the second part of the dissertation, we turn to the service pricing problem in a multi-provider setting: given service configurations (qualities) offered by different providers, choose a proper price for each offered service to undercut competitors and attract customers. With the rise of layered service-oriented architectures there is a need for more advanced solutions that manage the interactions among service providers at multiple levels. Brokers, as the intermediaries between customers and lower-level providers, play a key role in improving the efficiency of service-oriented structures by matching the demands of customers to the services of providers. We analyze a layered market in which service brokers and service providers compete in a Bertrand game at different levels in an oligopoly market while they offer different QoS. We examine the interaction among players and the effect of price competition on their market shares. We also study the market with partial cooperation, where a subset of players optimizes their total revenue instead of maximizing their own profit independently. We analyze the impact of this cooperation on the market and customers' social welfare

Théorie des jeux et apprentissage pour les réseaux sans fil distribués

Dans cette thèse, nous étudions des réseaux sans fil dans lesquels les terminaux mobiles sont autonomes dans le choix de leurs configurations de communication. Cette autonomie de décision peut notamment concerner le choix de la technologie d'accès au réseau, le choix du point d'accès, la modulation du signal, les bandes de fréquences occupées, la puissance du signal émis, etc. Typiquement, ces choix de configuration sont réalisés dans le but de maximiser des métriques de performances propres à chaque terminal. Sous l'hypothèse que les terminaux prennent leurs décisions de manière rationnelle afin de maximiser leurs performances, la théorie des jeux s'applique naturellement pour modéliser les interactions entre les décisions des différents terminaux. Plus précisément, l'objectif principal de cette thèse est d'étudier des stratégies d'équilibre de contrôle de puissance d'émission afin de satisfaire des considérations d'efficacité énergétique. Le cadre des jeux stochastiques est particulièrement adapté à ce problème et nous permet notamment de caractériser la région de performance atteignable pour toutes les stratégies de contrôle de puissance qui mènent à un état d'équilibre. Lorsque le nombre de terminaux en jeu est grand, nous faisons appel à la théorie des jeux à champ moyen pour simplifier l'étude du système. Cette théorie nous permet d'étudier non pas les interactions individuelles entre les terminaux, mais l'interaction de chaque terminal avec un champ moyen qui représente l'état global des autres terminaux. Des stratégies de contrôle de puissance optimales du jeu à champ moyen sont étudiées. Une autre partie de la thèse a été consacrée à des problématiques d'apprentissage de points d'équilibre dans les réseaux distribués. En particulier, après avoir caractérisé les positions d'équilibre d'un jeu de positionnement de points d'accès, nous montrons comment des dynamiques de meilleures réponses et d'apprentissage permettent de converger vers un équilibre. Enfin, pour un jeu de contrôle de puissance, la convergence des dynamiques de meilleures réponses vers des points d'équilibre a été étudiée. Il est notamment proposé un algorithme d'adaptation de puissance convergeant vers un équilibre avec une faible connaissance du réseau.In this thesis, we study wireless networks in which mobile terminals are free to choose their communication configuration. Theses configuration choices include access wireless technology, access point association, coding-modulation scheme, occupied bandwidth, power allocation, etc. Typically, these configuration choices are made to maximize some performance metrics associated to every terminals. Under the assumption that mobile terminals take their decisions in a rational manner, game theory can be applied to model the interactions between the terminals. Precisely, the main objective of this thesis is to study energy-efficient power control policies from which no terminal has an interest to deviate. The framework of stochastic games is particularly suited to this problem and allows to characterize the achievable utility region for equilibrium power control strategies. When the number of terminals in the network is large, we invoke mean field game theory to simplify the study of the system. Indeed, in a mean field game, the interactions between a player and all the other players are not considered individually. Instead, one only studies the interactions between each player and a mean field, which is the distribution of the states of all the other players. Optimal power control strategies from the mean field formulation are studied. Another part of this thesis has been focused on learning equilibria in distributed games. In particular, we show how best response dynamics and learning algorithms can converge to an equilibrium in a base station location game. For another scenario, namely a power control problem, we study the convergence of the best response dynamics. In this case, we propose a power control behavioral rule that converges to an equilibrium with very little information about the network.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

More about Base Station Location Games

ValueTools '11: 6th International Conference on Performance Evaluation Methodologies and Tools, May 2011, Paris, FranceInternational audienceThis paper addresses the problem of locating base stations in a certain area which is highly populated by mobile stations; each mobile station is assumed to select the closest base station. Base stations are modeled by players who choose their best location for maximizing their uplink throughput. The approach of this paper is to make some simplifying assumptions in order to get interpretable analytical results and insights to the problem under study. Specifically, a relatively complete Nash equilibrium (NE) analysis is conducted (existence, uniqueness, determination, and efficiency). Then, assuming that the base station location can be adjusted dynamically, the best-response dynamics and reinforcement learning algorithm are applied, discussed, and illustrated through numerical results