A game theoretic framework for bandwidth allocation

Most owners, however, encrypt their networks to prevent the public from accessing them due to the increased traffic and security risk. In this work, we use pricing as an incentive mechanism to motivate the owners to share their networks with the public, while at the same time satisfying users' service demand.

Specifically, we propose a "federated network" concept, in which radio resources of various wireless local area networks are managed together. Our algorithm identifies two candidate access points with the lowest price being offered if available to each user. The framework is based on the idea of the Nash bargaining solution from cooperative game theory, which not only provides the rate settings of users that are Pareto optimal from the point of view of the whole system, but are also consistent with the fairness axioms of game theory.

We first consider the centralized problem and then show that this procedure can be decentralized so that greedy optimization by users yields the system optimal bandwidth allocations. We have proposed a novel bandwidth allocation model based on game theory. The consideration of the user-grouping constraint distinguishes this model from the abundant ones concerning similar allocation issues.

Suppose each user competes for the system bandwidth resources and is granted with a constrained decision space. In particular, some users are united in one group and the total bandwidth allocated to the group is constrained as well. Given the appropriate constraint parameters and the utility function satisfying mild continuity and concavity conditions for each user, we have shown the unique existence of the user-grouping Nash equilibrium point for the allocation game.

In addition, we have shown the fairness, in a proper sense, of the allocation based on this equilibrium point. Finally, we have proposed an iterative algorithm and proved that a sequence converging to the point can be generated by the algorithm.

A practical example illustrating a network satisfying our settings has been given to show how the equilibrium point can be located successfully. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Received 02 Oct Revised 22 Dec Accepted 26 Dec Published 30 Jan Abstract A new bandwidth allocation model is studied in this paper. Introduction With the widespread use of internet and the increasing popularity of mobile devices, more and more people can get online at almost anytime and anywhere. Figure 1. The system bandwidth allocation subject to user-wise constraints: for , and a user-grouping constraint.

The total allocation is less than , the total bandwidth. Algorithm 1. Table 1. The parameters of for. User 1—10 11—20 21—30 31—70 71—90 91— Upper bound 50 78 Lower bound 10 20 30 5 10 Table 2. Figure 2. The sequence generated by Algorithm 1 for the example. References J. Anselmi, U. Ayesta, and A. Ayesta, O. Brun, and B. La and V. Richman and N. Boulogne, E. Altman, H. Kameda, and O. Korilis, A. Lazar, and A. View at: Google Scholar Y.

Niyato and E. Ganesh, K. Laevens, and R. Mazumdar, and C. View at: Google Scholar E. Altman, T. Boulogne, R. El-Azouzi, T. Bonomi and K. Lazar, A. Orda, and D. Evans and C. Rhee and T. Rhee, Optimal flow control and bandwidth allocation in multiservice networks: decentralized approaches [Ph.

Assi, Y. Ye, S. Dixit, and M. Xie, S. Jiang, and Y. Choi and J. Use of this web site signifies your agreement to the terms and conditions. A game theoretic framework for bandwidth allocation and pricing in broadband networks Abstract: In this paper, we present a game theoretic framework for bandwidth allocation for elastic services in high-speed networks.

The framework is based on the idea of the Nash bargaining solution from cooperative game theory, which not only provides the rate settings of users that are Pareto optimal from the point of view of the whole system, but are also consistent with the fairness axioms of game theory.