In this paper, we design a bandwidth pricing mechanism that solves congestion problems in communication networks. The scheme is based on second-price auctions, which are known to be incentive compatible when a single indivisible item is to be sold (users have no interest to lie about the price they are willing to pay for the resource) and to lead to an efficient allocation of resources in the sense that it maximizes social welfare. We prove these properties when an infinitely divisible resource (bandwidth on a communication link) is to be shared among users who are allowed to submit several bids when they want to establish a connection. Our scheme is highly related to the Progressive Second Price Auction of Lazar and Semret where players bid sequentially until an (optimal) equilibrium is reached. While keeping their incentive compatibility and efficiency properties, our scheme presents the advantage that the multi-bid is submitted once only, saving a lot of signalization overhead.

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The competition among wireless data service providers brings in an option for the customers to switch their providers, due to unsatisfactory service or otherwise. However, the existing resource management algorithms for wireless networks fail to fully capture the far-reaching impact of this competitiveness. From this perspective, we propose an integrated admission and rate control (ARC) framework for CDMA based wireless data networks. The admission control is at the session (macro) level while the rate control is at the link layer packet (micro) level. The ARC framework is based on a novel game theoretic formulation which defines non-cooperative games between the service providers and the customers. A user’s decision to leave or join a provider is based on a finite set of strategies. A service provider can also construct its game strategy set so as to maximize the utility (revenue) yet attaining

Abstract—We propose a novel payment-based incentive scheme for peer-to-peer (P2P) live media streaming. Using this approach, peers earn points by forwarding data to others. The data streaming is divided into fixed-length periods; during each of these periods, peers compete with each other for good parents (data suppliers) for the next period in a first-price-auction-like procedure using their points. We design a distributed algorithm to regulate peer competitions and consider various individual strategies for parent selection from a game-theoretic perspective. We then discuss possible strategies that can be used to maximize a peer’s expected media quality by planning different bids for its substreams. Finally, in order to encourage off-session users to remain online and continue contributing to the network, we develop an optimal data forwarding strategy that allows peers to accumulate points that can be used in future services. Simulation results show that the proposed methods effectively differentiate the media qualities received by peers making different contributions (which originate from, for example, different forwarding bandwidths or servicing times) and at the same time maintain high overall system performance. Index Terms—Peer-to-peer, media streaming, incentive, service differentiation, payment. 1

Selfish overlay networks consist of autonomous nodes that develop their own strategies by optimizing towards their local objectives and self-interests, rather than following prescribed protocols. It is thus important to regulate the behavior of selfish nodes, so that system-wide properties are optimized. In this paper, we investigate the problem of bandwidth allocation in overlay networks, and propose to use a market-driven approach to regulate the behavior of selfish nodes that either provide or consume services. In such markets, consumers of services select the best service providers, taking into account both the performance and the price of the service. On the other hand, service providers are encouraged to strategically decide their respective prices in a pricing game, in order to maximize their economic revenues and minimize losses in the long run. In order to overcome the limitations of previous models towards similar objectives, we design a decentralized algorithm that uses reinforcement learning to help selfish nodes to incrementally adapt to the local market, and to make optimized strategic decisions based on past experiences. We have simulated our proposed algorithm in randomly generated overlay networks, and have shown that the behavior of selfish nodes converges to their optimal strategies, and resource allocations in the entire overlay are near-optimal, and efficiently adapts to the dynamics of overlay networks.

One of the key issues for implementing congestion pricing is the pricing granularity (i.e. pricing interval or time-scale). The Internet traffic is highly variant and hard to control without a mechanism that operates on very low time-scales, i.e. on the order of round-trip-times (RTTs). However, pricing naturally operates on very large time-scales because of human involvement. Moreover, structure of wide-area networks does not allow frequent price updates for many reasons, such as RTTs are very large for some cases. In this paper, we investigate the issue of pricing granularity and identify problems. We first focus on how much level of control over congestion can be achieved by congestion pricing. To represent the level of control over congestion, we use correlation between prices and congestion measures. We develop analytical and statistical models for the correlation. In order to validate the correlation model, we develop packet-based simulation of our congestion pricing scheme Dynamic Capacity Contracting. We then present the fit between simulation results of the pricing scheme and the correlation model. The correlation model reveals that the correlation degrades at most inversely proportional to an increase in the pricing interval. It also reveals that the correlation degrades with an increase in mean or variance of the traffic. Secondly, we discuss implications of the correlation model. According to the model and simulation results, we find that control of congestion by pricing degrades significantly as pricing granularity increases.

Several congestion pricing proposals have been made in the last decade. Usually, however, those proposals studied optimal strategies and did not focus on implementation issues. Our main contribution in this paper is to address implementation issues for congestion-sensitive pricing over a single domain of the differentiated-services (diff-serv) architecture of the Internet. We propose a new congestion-sensitive pricing framework Distributed Dynamic Capacity Contracting (Distributed-DCC), which is able to provide a range of fairness (e.g. max-min, proportional) in rate allocation by using pricing as a tool. Within the Distributed-DCC framework, we develop two pricing schemes based on the manner of using pricing to control congestion: Pricing for Congestion Control (PFCC) and Pricing over Congestion Control (POCC). PFCC uses pricing directly for controlling congestion, whilst POCC uses an underlying edge-to-edge congestion control mechanism by overlaying pricing on top of it.

This article investigates the role and importance of the economic aspects that are vital to the success of wireless services deployment and provider selection by users in a competitive environment. We show how some of the econometric measures can meaningfully capture the user decisions/actions (e.g., churning) that can potentially be utilized by the providers in managing radio resources (e.g., bandwidth) in wireless data networks. In particular, by modeling the interaction between a service provider and its customers (or users) as a non-cooperative game, we propose a novel cross-layer resource management framework for integrated admission and rate control in CDMA networks. Analytical and simulation results demonstrate how the proposed framework can help minimize customer churning and maximize revenue for the wireless operators, yet optimizing customer satisfaction by providing differentiated quality of service to different classes of users. The tremendous advancement in wireless communications technology, such as the third generation (3G)

We present a two level modeling approach for DiffServ mechanisms. We first sketch a flow model to capture the user requirements of differentiation and to compare differentiation to traditional best-effort TCP flow level results. We then study how the bandwidth allocations presented on the flow level can be modeled and achieved on the packet level. The DiffServ mechanism we use is the Simple Integrated Media Access (SIMA) proposal. We choose SIMA, as it employs both financial and end-to-end congestion control incentives, and is thus both a flow level and packet level mechanism. As a result of our two level modeling, we show that the differentiation achieved by SIMA is within fixed bounds predetermined by the acquired nominal bit rate of a flow. We further enhance our model to show, how the SIMA priority mechanism is also able to protect TCP flows from bandwidth exhaustion by non-TCP flows.

Over the past ten years, many pricing schemes have been proposed for a QoS-enabled network. Most of the proposed QoS-pricing schemes focus on congestion-sensitive pricing and optimal pricing solutions. Integrating pricing and admission control has not been studied in details. In this paper, we pay more attention to the interrelation between pricing and admission control in QoS-enabled networks and propose a tari#-based architecture framework that flexibly integrates pricing and admission control for multi-domain Di#Serv networks. We study the pricing and user behaviors in detail and design a market-regulated pricing and admission control scheme in our framework. We model the system as a market so that the price of a service class reflects the resource availability inside the network and is regulated by the market itself. We also evaluate our pricing strategy and admission control scheme through simulations.