The Differentiated Services framework (DiffServ) has been proposed to provide multiple Quality of Service (QoS) classes over IP networks. A network supporting multiple classes of service also requires a differentiated pricing structure. We propose a pricing scheme in a DiffServ environment based on the cost of providing different levels of quality of service to different classes, and on long-term demand. Pricing of network services dynamically based on the level of service, usage, and congestion allows a more competitive price to be offered, allows the network to be used more efficiently, and provides a natural and equitable incentive for applications to adapt their service contract according to network conditions. We develop a DiffServ simulation framework to compare the performance of a network supporting congestion-sensitive pricing and adaptive service negotiation to that of a network with a static pricing policy. Adaptive users adapt to price changes by adjusting their sending rate or selecting a different service class. We also develop the demand behavior of adaptive users based on a perceptually reasonable user utility function. Simulation results show that a congestion-sensitive pricing policy coupled with user rate adaptation is able to control congestion and allow a service class to meet its performance assurances under large or bursty offered loads, even without explicit admission control. Users are able to maintain a stable expenditure. Allowing users to migrate between service classes in response to price increases further stabilizes the individual service prices. When admission control is enforced, congestion-sensitive pricing still provides an advantage in terms of a much lower connection blocking rate at high loads. I.

In this paper, we study the design tradeoffs involved in video streaming in networks with QoS guarantees. We approach this problem by using a utility function to quantify the benefit a user derives from the received video sequence. This benefit is expressed as a function of the total distortion. In addition, we also consider the cost, in network resources, of a video streaming system. The goal of the network user is then to obtain the most benefit for the smallest cost. We formulate this utility maximization problem as a joint constrained optimization problem. The difference between the utility and the network cost is maximized subject to the constraint that the decoder buffer does not underflow. We present a deterministic dynamic programming approach to find the optimal tradeoff for both the Constant Bit Rate (###) and Renegotiated Constant Bit Rate (####) service classes. Experimental results demonstrate the benefits and the performance of the proposed approach. 1.

In a network with enhancements for QoS support, pricing of network services based on the level of service, usage, and congestion provides a natural and equitable incentive for applications to adapt their sending rates according to network conditions. In this paper, we first propose a dynamic, congestion-sensitive pricing algorithm, and also develop the demand behavior of adaptive users based on a physically reasonable user utility function. We then develop a simulation framework to compare the performance of a network supporting congestion-sensitive pricing and adaptive reservation to that of a network with a static pricing policy. We also study the stability of the dynamic pricing and reservation mechanisms, and the impact of various network control parameters. The results show that the congestionsensitive pricing system takes advantage of application adaptivity to achieve significant gains in network availability, revenue, and user-perceived benefit relative to the fixed-price policy. Congestion-based pricing is stable and effective in limiting utilization to a targeted level. Users with different demand elasticity are seen to share bandwidth fairly, with each user having a bandwidth share proportional to its relative willingness to pay for bandwidth. The results also show that even a small proportion of adaptive users may result in a significant performance benefit and better service for the entire user population - both adaptive and non-adaptive users. The performance improvement given by the congestion-based adaptive policy further improves as the network scales and more connections share the resources.

In order to provide better Quality-of-Service (QoS) in large networks, 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 an Edge-to-Edge Pricing Scheme (EEP) and present simulation experiments of it.

Abstract — Continuous media, such as digital movies, video clips, and music, are becoming an increasingly common way to convey information, entertain and educate people. However, limited system and network resources have delayed the widespread usage of continuous media. Most existing on-demand video services, such as VoD, batching and patching, are not scalable. Although the Near VoD service is scalable, it can provide only dozens of videos. In this paper, we propose a scalable and inexpensive video delivery paradigm, named Scheduled Video Delivery (SVD). In the SVD paradigm, users submit requests with specified start time. The SVD system combines requests to form multicasting groups and schedules these groups to meet the deadline. SVD scheduling has a different objective from many existing scheduling schemes. It does not aim at minimizing the waiting time. Instead, it focuses on meeting deadlines and at the same time combining requests to form multicasting groups. SVD provides scalable on-demand video service with much less channels. 1

Charging a higher price during periods of congestion should more efficiently allocate scarce resources by encouraging users to conserve. We study its benefits by conducting several pricing experiments over two semesters with students in the dormitories using a computer-telephony-service. Users can use the service to make and receive phone calls from their computers or phones. While we do not charge users real money, we limit each user to a certain number of tokens a week. With this experimental setup, we conducted a different pricing experiment each week to better understand how prices can be used to entice users to talk less, talk at another time, or use a lower quality connection.

Abstract — This paper describes and formalises the Service

The deployment of the Virtual Home Environment (VHE) concept in 3G mobile systems in the near future will place many demands on managing a user's personalised service environment. Key to the VHE concept is the performance of such services, resulting in a need to manage Quality of Service (QoS) demands and allocation on behalf of users. This paper examines the issues involved in managing QoS demands from the various roles and entities in the VHE and proposes a policybased framework to satisfy their needs.

Abstract—Development in wireless access technologies and multihomed personal user devices is driving the way towards a heterogeneous wireless access network environment. Success in this arena will be reliant on the ability to offer an enhanced user experience. Users will plan to take advantage of the competition and always connect to the network which can best service their preferences for the current application. They will rely on intelligent network selection decision strategies to aid them in their choice. The contribution of this paper is to propose an intelligent utility-based strategy for network selection in this multi-access network scenario. A number of utility functions are examined which explore different user attitudes to risk for money and delay preferences related to their current application. For example we show that risk takers who are willing to pay more money get a better service.

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.