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.
@cite_0: 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.
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.