Optimal pricing for multiple services in telecommunications networks offering quality-of-service guarantees

We consider pricing for multiple services offered over a single telecommunications network. Each service has quality-of-service (QoS) requirements that are guaranteed to users. Service classes may be defined by the type of service, such as voice, video, or data, as well as the origin and destination of the connection provided to the user. We formulate the optimal pricing problem as a nonlinear integer expected revenue optimization problem. We simultaneously solve for prices and the resource allocations necessary to provide connections with guaranteed QoS. We derive optimality conditions and a solution method for this class of problems, and apply to a realistic model of a multiservice communications network.     

Pricing congestible network resources

We describe the basic economic theory of pricing a congestible resource such as an FTP server, a router, a Web site, etc. In particular, we examine the implications of “congestion pricing” as a way to encourage efficient use of network resources. We explore the implications of flat pricing and congestion pricing for capacity expansion in centrally planned, competitive, and monopolistic environments. The most common form of Internet pricing is pricing by access, with no usage-sensitive prices. With a fixed set of users, we expect to see greater capacity when usage is not priced, but also greater congestion. However, with greater congestion, congestion-sensitive users might not use the resource     

Pricing for QoS-enabled networks: A survey

A complete solution to the problem of providing adequate quality of service (QoS) to heterogeneous users must take into account the issue of pricing. By adopting an appropriate pricing policy and by setting prices carefully, a service provider will be able to offer the necessary incentives for each user to choose the service that best matches his⁄her needs, thereby discouraging over-allocation of resources and maximizing revenue and⁄or social welfare. In this article, we survey some of the recent research in the literature dealing with service pricing in multi-service networks. All of the work surveyed here addresses the relationship between prices and traffic management functions such as congestion control, resource provisioning, and call admission control. We summarize proposed pricing policies for the Internet and for ATM networks, as well as some studies of pricing for general QoS-enabled networks.     

A game-theoretic approach to decentralized optimal power allocation for cellular networks

The rapidly growing demand for wireless communication makes efficient power allocation a critical factor in the network??s efficient operation. Power allocation in cellular networks with interference, where users are selfish, has been recently studied by pricing methods. However, pricing methods do not result in efficient/optimal power allocations for such systems for the following reason. Because of interference, the communication between the Base Station (BS) and a given user is affected by that between the BS and all other users. Thus, the power vector consisting of the transmission power in each BS-user link can be viewed as a public good which simultaneously affects the utilities of all the users in the network. It is well known (Mas-Colell et al., Microeconomic Theory, Oxford University Press, London, 2002, Chap. 11.C) that in public good economies, standard efficiency theorems on market equilibrium do not apply and pricing mechanisms do not result in globally optimal allocations. In this paper we study power allocation in the presence of interference for a single cell wireless Code Division Multiple Access (CDMA) network from a game theoretic perspective. We consider a network where each user knows only its own utility and the channel gain from the base station to itself. We formulate the uplink power allocation problem as a public good allocation problem. We present a game form the Nash Equilibria of which yield power allocations that are optimal solutions of the corresponding centralized uplink network.     

Single-cell forward link power allocation using pricing in wireless networks

We consider forward link power allocation for voice users in a code-division multiple-access wireless network. Admission control policies are investigated, which base a new call admission decision not only upon available capacity, but also upon the required forward link transmit power and upon the user's willingness to pay. We assume that each voice user has a utility function that describes the user's willingness to pay as a function of forward link signal-to-interference plus noise ratio. The network objective is to maximize either total utility summed over all users or total revenue generated from all users. Properties of the optimal power and code allocations are presented. Our key results show how these optimal allocations can be achieved using pricing. The analysis is complemented with a numerical study, which shows how the optimal prices and corresponding utility or revenue vary with load.     

Deadlock Detection and Avoidance Strategies for Automated Storage and Retrieval Systems

This paper focuses on real-time control of automated storage and retrieval systems (AS/RSs) serviced by a rail-guided vehicle system, a widely used solution for material handling in warehouses. The generic multiproduct AS/RS is modeled as a timed discrete event dynamical system, whose state provides the information on the current interactions between users and resources. Moreover, we address the real-time controller that governs resource allocations and scheduling choices by enabling and inhibiting the system events in order to avoid collisions and deadlocks. To this aim, we characterize deadlock in AS/RSs and define two deadlock resolution strategies: a deadlock avoidance and a deadlock detection/recovery policy. The proposed deadlock formulation and characterization have a general validity and can be applied to single unit resource allocation systems where a subset of users may be regarded as resources of other users. We compare the proposed control policies for a large-scale AS/RS presented in the related literature by several discrete event simulation tests.     

一种基于定价与信任的网格资源分配算法

该文首先对基于组合双向拍卖的网格资源分配与定价模型进行改进,提出以各类资源的单价定价的算法。然后提出一种等效价格算法,设计基于信任度的价格调整函数,将不同信任度的各节点的报价,都映射为基准信任度下的等效价格。最后基于该等效报价,以组合双向拍卖模型进行网格资源分配。仿真表明所提算法的交易率较高,能防止恶意节点参与交易,所得交易效用可激励买家提高和卖家降低其等效报价。     

基于SDN框架的网络资源定价策略

针对网络中用户集中提出资源请求所造成资源负载过重和网络拥塞的问题,提出一种基于SDN架构的网络资源定价策略。首先,分析用户的需求和消费模式,确定资源交易模型。其次,结合现货消费模式和计划消费确定预约机制下资源动态定价策略模型。最后,根据所提出的资源定价策略,对资源价格进行实例仿真,结果表明,通过价格刺激用户提前预约资源,结合每个用户的信任度与消费习惯给出相应的优惠价格,可以有效地避免用户集中获取网络资源。     

Pricing in computer networks: motivation, formulation, and example

The role of pricing policies in multiple service class networks is studied. An abstract formulation of service disciplines and pricing policies that allows the interplay between service disciplines and pricing policies in determining overall network performance to be described more clearly is presented. Effective multiclass service disciplines allow networks to focus resources on performance-sensitive applications, while effective pricing policies allows the benefits of multiple service classes to be spread around to all users. Furthermore, the incentives formed by service disciplines and pricing policies must be carefully tuned so that user self-interest leads to optimal overall network performance. These concepts are illustrated through simulation of several simple example networks. It is found that it is possible to set the prices so that users of every application type are more satisfied with the combined cost and performance of a network with service-class-sensitive prices     

Utility-based rate control in the Internet for elastic traffic

In a communication network, a good rate allocation algorithm should reflect the utilities of the users while being fair. We investigate this fundamental problem of achieving the system optimal rates in the sense of maximizing aggregate utility, in a distributed manner, using only the information available at the end hosts of the network. This is done by decomposing the overall system problem into subproblems for the network and for the individual users by introducing a pricing scheme. The users are to solve the problem of maximizing individual net utility, which is the utility less the amount they pay. We provide algorithms for the network to adjust its prices and the users to adjust their window sizes such that at an equilibrium the system optimum is achieved. Further, the equilibrium prices are such that the system optimum achieves weighted proportional fairness. It is notable that the update algorithms of the users do not require any explicit feedback from the network, rendering them easily deployable over the Internet. Our scheme is incentive compatible in that there is no benefit to the users to lie about their utilities     

Joint network-centric and user-centric radio resource management in a multicell system

A pricing mechanism to mediate (and allocate resources) between conflicting user and network objectives has been recently proposed by the authors in a single-cell system. Here, we extend the results to a multicell system, where the autonomous base station assignment and power control are formulated as a noncooperative game among users. The network prices the resources using two strategies: global pricing that maximizes the revenue, and minimax pricing that trades off the revenue for a more even resource allocation.     

Efficiency and Braess' Paradox under pricing in general networks

We study the flow control and routing decisions of self-interested users in a general congested network where a single profit-maximizing service provider sets prices for different paths in the network. We define an equilibrium of the user choices. We then define the monopoly equilibrium (ME) as the equilibrium prices set by the service provider and the corresponding user equilibrium. We analyze the networks containing different types of user utilities: elastic or inelastic. For a network containing inelastic user utilities, we show the flow allocations at the ME and the social optimum are the same. For a network containing elastic user utilities, we explicitly characterize the ME and study its performance relative to the user equilibrium at 0 prices and the social optimum that would result from centrally maximizing the aggregate system utility. We also define Braess' Paradox for a network involving pricing and show that Braess' Paradox does not occur under monopoly prices.     

Incentive pricing in multiclass systems

In modern communication networks which offer multiple classes of services, an appropriate assignment of service classes to users (or applications) will have a key influence on the performance profile. Differentiated pricing is an important tool for guiding the user's choice. We consider a basic model for a multiclass system that offers multiclass services to multiple types of traffic, and propose a pricing framework which is based on the concept of nominal traffic assignment. Users (or their associated traffic) are categorized into a finite number of traffic types, which are distinct in their performance utilities at the different service classes. The system administrator specifies a required traffic assignment, which associates with each traffic type a nominal service class. Users, on the other hand, choose service classes so as to optimize their own performance. Optimal prices should provide incentives for the users to assign each traffic type to its nominal service class. Our goal is to implement a simple pricing scheme that provides such incentives. We establish necessary and sufficient conditions for the existence of optimal prices and provide an algorithm for their computation. We indicate that optimal prices can tolerate fluctuations in the various parameters. Next, we propose a distributed algorithm, which can be used by the system to compute optimal prices even when it does not have sufficient knowledge of traffic characteristics. We then generalize our analysis to an extended model, which explicitly includes congestion effects. This revised version was published online in June 2006 with corrections to the Cover Date.     

An Adaptable Job Submission System Based on Moderate Price-Adjusting Policy in Market-Based Grids

A market-based computational grid is made up of large sets of heterogeneous and geographically distributed resources that are gathered into virtual organizations for executing consumer’s applications. One of the most important challenges in market-based grid systems is the management of grid users, which is called resource providers and consumers. The existing methods provide some alternative mechanisms for this problem, but they are not fully adequate. To address this problem, we propose an enhanced approach for adjusting price of grid resource using new effective parameters of microeconomic issue and also for prioritizing current jobs in the queue. This proposed approach is integrated with a cooperative method among local schedulers to accept jobs based on auction model. The results conclude that the inclusion of new parameters in price-adjusting affects the payment budget and job submission behavior of the schedulers. The evaluations of experimental results prove a remarkable performance of the proposed approach in diverse conditions and job workloads.     

Providing Internet access: what we learn from INDEX

The Internet demand experiment, or INDEX, is a market and technology trial. Its objective is to determine how much users value different qualities of service for Internet access. Findings from the trial imply that today's system of flat-rate pricing by ISPs is very inefficient. Flat-rate pricing wastes resources, requires light users to subsidize heavy users, and hinders deployment of broadband access. INDEX is a prototype of an alternative ISP model that offers differentiated quality service on demand, with prices that reflect resource cost. In this alternative ISP consumers pay less, suppliers increase profits, and the deployment of broadband access is facilitated     

A game theoretic framework for bandwidth allocation and pricing inbroadband networks

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. 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 propose a distributed algorithm for implementing the optimal and fair bandwidth allocation and provide conditions for its convergence. The paper concludes with the pricing of elastic connections based on users' bandwidth requirements and users' budget. We show that the above bargaining framework can be used to characterize a rate allocation and a pricing policy which takes into account users' budget in a fair way and such that the total network revenue is maximized     

An optimal real‐time pricing algorithm under load uncertainty and user number variation in smart grid

The progress of real‐time communication systems for smart grid has led to the importance of real‐time pricing becoming more highlighted. There are many investigations that have already been done. Real‐time pricing frameworks have proposed an implemented distributed algorithm with or without consideration of effect of load uncertainty. In some existing literature the effect of different types of load uncertainty models on average consumption and generated capacity is considered. However, the number of users is considered to be constant. In this paper, it is assumed that the number of users is varying independently and randomly. In this case the effect of variation of number of users on the basis of Poisson process and uniform distribution is compared with results from previous works, when bounded uncertainty model was applied for added noise to the consumption. Simulation results indicate that when users vary on the basis of Poisson distribution, the waste of energy decreases and the welfare increases.     

An adaptive Paris Metro Pricing scheme for mobile data networks

Because static pricing models (such as flat‐rate or tiered‐rate models) cannot improve user utility for subscribers and ease network congestion for operators during peak time, Smart Data Pricing has become an important incentive for mobile data markets. Paris Metro Pricing (PMP), which is a static pricing mode inspired by the pricing model for the Paris metro system, uses differentiated prices to motivate users to choose different train classes. Before choosing a class, people will consider their expected quality of service versus the prices that they are willing to pay. Even though PMP cannot guarantee the actual quality of service during service time, a balance between users' utilities and operators' revenue is achieved. In this paper, we propose an adaptive PMP scheme, so‐called APMP, which determines the dynamic access prices of different classes for the next 24 h. The accessible prices should try to increase the revenue while operators can serve more subscribers. Our simulation results show that APMP can significantly improve total revenue and average revenue per user for the operator. Copyright © 2016 John Wiley & Sons, Ltd.     

Cooperation and Fairness for Slotted Aloha

Wireless access based on slotted Aloha with selfish users may result in very inefficient use of the system resources. To impose cooperation and fairness in such systems, we propose an optimal pricing strategy, based on which the service provider can regulate the overall network behavior. As the users’ utility incorporates the price paid for using the spectrum, by striving to improve their own performance, the users act to optimize the overall network performance. Our analysis is based on a game theoretic framework, and we consider both the simple collision model for packet reception, as well as multipacket reception capabilities for the physical layer. The proposed pricing strategy enforces fairness under the constraint of an equal access probability.     

Resource planning and incentive engineering for a congested wireless access point: an integrated multiple time scale control mechanism

Wireless networks are playing an increasingly important role for global communications. Many resource allocation mechanisms have been proposed to efficiently utilize the limited radio resources in wireless networks to support a large number of mobile users with a diversity of applications. Among them, pricing frameworks that provide incentives to users to maximize their individual utility while optimizing allocation of network resources have attracted a lot of attention recently. Nevertheless, most of these pricing schemes require dynamic charging rates and may be too complex for wide acceptance by users, as most users would prefer relatively simple charging schemes. Moreover, use of a pricing framework to facilitate resource planning and future expansion at the service provider’s side has not yet been widely considered. In this paper, we propose Integrated Multiple Time Scale Control (IMTSC), a novel incentive engineering mechanism to facilitate resource allocation and network planning. Over different time scales, IMTSC combines the functions of network capacity planning, admission control for resource allocation, and tracking of users’ instantaneous traffic demands. The proposed mechanism is applied for access control at a congested access point in a wireless network. By decomposing the original problem into distributed optimization problems that are solved locally by the service provider through adjusting charging rate and remotely by individual users by appropriately changing her service requests, we show that maximization of user’s utility and increase of network efficiency can be simultaneously achieved. Results from extensive simulations demonstrate the effectiveness of the proposed IMTSC mechanism.