Abstract In this paper we introduce a capacity allocation game which models the problem of maximizing network utility from the perspective of distributed noncooperative agents. Motivated by the idea of self-managed networks, in the developed framework the decision-making entities are associated with individual transmission links, deciding on the way they split capacity among concurrent flows. An efficient decentralized algorithm is given for computing a strongly Pareto-optimal strategies, constituting a pure Nash equilibrium. Subsequently, we discuss the properties of the introduced game related to the Price of Anarchy and Price of Stability. The paper is concluded with an experimental study.
The joint problem of QoS routing and capacity allocation problem is considered in @cite_0 . In the proposed game, two groups of players are introduced, namely capacity players (each related to one link) and network users (each related to one pair of source and destination). Each capacity player divides its capacity among given Class of Services to minimize overall congestion over the associated link. On the other hand each user splits their traffic among all available paths so as to maximize a degree of satisfaction.
@cite_0
2039411283 2099285351 2136340918 1979708789
Efficient dynamic resource provisioning algorithms are necessary to the development and automation of Quality of Service (QoS) networks. The main goal of these algorithms is to offer services that satisfy the QoS requirements of individual users while guaranteeing at the same time an efficient utilization of network resources. This paper proposes a novel game theoretical model that solves the joint problem of non-cooperative QoS routing and dynamic capacity allocation in a parallel links network. Two categories of players are introduced: (1) the capacity players that dimension the link capacities to provide QoS guarantees to users, minimizing, at the same time, the links' congestion, and (2) network users, which are characterized by elastic traffic demands and split their traffic over multiple links, maximizing their objective function. This game is modeled as a multi-leader-follower game, where capacity players are leaders and network users are followers. We derive optimal routing and capacity settings using a round robin greedy algorithm, discussing numerical examples that provide insights into the model's solution. We consider the distributed uplink resource allocation problem in a multi-carrier wireless network with multiple access points (APs). Each mobile user can optimize its own transmission rate by selecting a suitable AP and by controlling its transmit power. Our objective is to devise suitable algorithms by which mobile users can jointly perform these tasks in a distributed manner. Our approach relies on a game theoretic formulation of the joint power control and AP selection problem. In the proposed game, each user is a player with an associated strategy containing a discrete variable (the AP selection decision) and a continuous vector (the power allocation among multiple channels). We provide characterizations of the Nash Equilibrium of the proposed game. We present a novel algorithm named Joint Access Point Selection and Power Allocation (JASPA) and its various extensions (with different update schedules) that allow the users to efficiently optimize their rates. Finally, we study the properties of the proposed algorithms as well as their performance via extensive simulations. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users. Abstract This paper studies the quality of service (QoS) provision problem in noncooperative networks where applications or users are selfish and routers implement generalized processor sharing based packet scheduling. We formulate a model of QoS provision in noncooperative networks where users are given the freedom to choose both the service classes and traffic volume allocated, and heterogenous QoS preferences are captured by a user's utility function. We present a comprehensive analysis of the noncooperative multi-class QoS provision game, giving a complete characterization of Nash equilibria and their existence criteria, and show under what conditions they are Pareto- and system-optimal. We show that, in general, Nash equilibria need not exist, and when they do exist, they need not be Pareto- nor system-optimal. For certain “resource-plentiful” systems, however, we show that the world indeed can be nice with Nash equilibria, Pareto optima, and system optima collapsing into a single class. We study the problem of facilitating effective QoS in systems with multi-dimensional QoS vectors containing both mean- and burstiness-related QoS measures. We extend the game-theoretic analysis to multi-dimensional QoS vector games and show under what conditions the aforementioned results carry over.