Internet and graphs are very much related. The graphical structure of internet has been studied extensively to provide efficient solutions to routing and other problems. But most of these studies assume a central authority which controls and manages the internet. In the recent years game theoretic models have been proposed which do not require a central authority and the users are assumed to be routing their flows selfishly. The existence of Nash Equilibria, congestion and the amount of inefficiency caused by this selfish routing is a major concern in this field. A type of paradox in the selfish routing networks, Braess' Paradox, first discovered by Braess, is a major contributor to inefficiency. Several pricing mechanisms have also been provided which give a game theoretical model between users(consumers) and ISPs ( Internet Service Providers or sellers) for the internet. We propose a novel pricing mechanism, based on real world Internet network architecture, which reduces the severity of Braess' Paradox in selfish routing game theoretic networks. It's a pricing mechanism between combinatorial users and ISPs. We prove that Nash equilibria exists in this network and provide bounds on inefficiency . We use graphical properties of internet to prove our result. Several interesting extensions and future work have also been discussed.
Hayrapetyan et. al @cite_10 analyze among similar lines as us but for their cost function they assume a constant term for the per unit flow price charged by ISPs but in the real world scenario generally the per unit charge decreases if the flow required by the user increases i.e. ISPs provide concession for more flow. Our model captures this. They have not done any analysis of the effect of Braess' Paradox in their network, too. We show that in our model the severity of Braess' Paradox is reduced.
@cite_10
2120126835 2099797210 1991014770 2057559829
We consider a network where each user is charged a fixed price per unit of bandwidth used, but where there is no congestion-dependent pricing. However, the transmission rate of each user is assumed to be a function of network congestion (like TCP), and the price per unit bandwidth. We are interested in answering the following question: how should the network choose the price to maximize its overall revenue? To obtain a tractable solution, we consider a single link accessed by many users where the capacity is increased in proportion to the number of users. We show the following result: as the number of users increases, the optimal price per unit bandwidth charged by the service provider may increase or decrease depending upon the bandwidth of the link. However, for all values of the link capacity, the service provider's revenue per unit bandwidth increases and the overall performance of each user (measured in terms of a function of its throughput, the network congestion and the cost incurred by the user for bandwidth usage) improves. Since the revenue per unit bandwidth increases, it provides an incentive for the service provider to increase the available bandwidth in proportion to the number of users. Usage-based pricing of offered traffic to a data network can be an effective technique for congestion control. To gain insight into the benefits usage-based pricing offers, the authors propose and study a simple model in which many users wish to transmit packets to a single-server queue. Based on the announced price per packet and the available quality of service (QoS) (e.g., mean delay), each user independently decides whether or not to transmit. Given statistical assumptions about the incoming traffic streams and the QoS as a function of offered load, the equilibrium relationship between price and QoS is determined by a fixed-point equation. The relationships among price, QoS, revenue, and server capacity are illustrated numerically, assuming a particular type of random user population. These examples indicate that adjusting the price to maximize revenue results in an efficient use of service capacity with an associated small mean delay. In this paper, we present a combined study of price competition and traffic control in a congested network. We study a model in which service providers own the routes in a network and set prices to maximize their profits, while users choose the amount of flow to send and the routing of the flow according to Wardrop's principle. When utility functions of users are concave and have concave first derivatives, we characterize a tight bound of 2-3 on efficiency in pure strategy equilibria of the price competition game. We obtain the same bound under the assumption that there is no fixed latency cost, i.e., the latency of a link at zero flow is equal to zero. These bounds are tight even when the numbers of routes and service providers are arbitrarily large. © 2008 Wiley Periodicals, Inc. NETWORKS, 2008 Network congestion is a fundamental problem facing Internet users today. A network where users are selfish, and thus reluctant to defer to other users, may result in the famous "tragedy of the commons", where, in the absence of controls, a shared resource is overconsumed by individuals who consider only their personal costs and not the cost to society as a whole. In terms of the Internet, the "tragedy" could be viewed as congestive collapse, resulting from overconsumption of the shared network resource. It is important to distinguish congestion pricing from other forms of network pricing. Charging network users for the congestion they cause can lead to more efficient network utilization by forcing them to take social costs into account. In a congestion-pricing framework, the congestion charge would replace usage and QoS charges. Users would pay their ISPs a subscription charge to cover fixed costs and a congestion charge only when appropriate. This pricing scheme is feasible because, in the absence of congestion, the marginal cost of a network link is practically zero. Congestion pricing can also benefit network operators. By indicating the level of congestion and the user tolerance of it in their networks, congestion pricing can inform operators about when to re-provision and increase network capacity.