Congestion-dependent pricing of network services

We consider a service provider (SP) who provides access to a communication network or some other form of on-line services. Users initiate calls that belong to a set of diverse service classes, differing in resource requirements, demand pattern, and call duration. The SP charges a fee per call, which can depend on the current congestion level, and which affects users' demand for calls. We provide a dynamic programming formulation of the problems of revenue and welfare maximization, and derive some qualitative properties of the optimal solution. We also provide a number of approximate approaches, together with an analysis that indicates that near-optimality is obtained for the case of many, relatively small, users. In particular, we show analytically as well as computationally, that the performance of an optimal pricing strategy is closely matched by a suitably chosen static price, which does not depend on instantaneous congestion. This indicates that the easily implementable time-of-day pricing will often suffice. Throughout, we compare the alternative formulations involving revenue or welfare maximization, respectively, and draw some qualitative conclusions     

Simplification of Network Dynamics in Large Systems

We show that when networks are large significant simplicity can be achieved for pricing-based control. We first consider a general loss network with Poisson arrivals and arbitrary holding time distributions. In dynamic pricing schemes, the network provider can charge different prices to the user according to the current utilization level of the network and also other factors. We show that when the system becomes large the performance (in terms of expected revenue) of an appropriately chosen static pricing scheme, whose price is independent of the current network utilization, will approach that of the optimal dynamic pricing scheme. Further, we show that under certain conditions, this static price is independent of the route that the flows take. We then extend the result to the case of dynamic routing, and show that the performance of an appropriately chosen static pricing scheme with bifurcation probability determined by average parameters can also approach that of the optimal dynamic routing scheme when the system is large. These results deepen our understanding of pricing-based network control. In particular, they provide us with the insight that, when the system is large, an appropriate pricing strategy based on the average network conditions (hence, slowly changing) can approach optimality.     

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.     

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.     

Pricing network resources for adaptive applications

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. In this work, we propose a pricing algorithm in a DiffServ environment based on the cost of providing different levels of services, and on long-term average user resource demand of a service class. We integrate the proposed service-dependent pricing scheme with a dynamic pricing and service negotiation environment by considering a dynamic and congestion-sensitive pricing component. Pricing 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 requests according to network conditions. We also develop the demand behavior of adaptive users based on a physically reasonable user utility function. Simulation results show that a congestion-sensitive pricing policy coupled with user rate adaptation is able to control congestion and allows 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, and 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.     

基于Stackelberg博弈的WCDMA网络收益最大化计费的研究

该文研究了在WCDMA网络中如何选择价格来最大化网络收益.没有采用拥塞相关的计费,而是对每个用户有效传输的单位吞吐量收取固定的费用,但每个用户的传输速率是网络拥塞和单位带宽价格的函数,并在此基础上提出了用户净效用函数.利用Stackelberg博弈,建模网络与用户之间的交互,即一方面网络管理者设定价格,以便实现收益最大化,而用户通过自优化效用函数来寻找新的均衡点对此做出响应.本文提供了网络收益与接纳用户数目的定量关系,并研究了网络降低用户传输速率以增大网络容量和拥塞控制的经济动机.     

多业务通信网络中的最佳收益计费和网络资源分配模型

文章利用Nash均衡模型和Stackelberg模型探讨了服务提供商如何在不同类型的业务中合理地分配网络资源,以及对各类业务制定合理的价格,以实现服务提供商和用户双方的收益最大化,并且讨论了网络资源和用户的需求对价格的影响和对用户服务业务类型选择的影响.     

Pricing and power control for joint network-centric and user-centric radio resource management

Objectives of most radio resource-management schemes can be classified as either user centric or network centric. User-centric schemes try to maximize the interests of individual users, while network-centric schemes optimize collective metrics for all users. These two types of resource management tend to result in qualitatively different resource allocations (with, sometimes, very different degrees of fairness). In this paper, we consider the joint optimization of both user-centric and network-centric metrics. Specifically, we use a utility function (measured in units of bits per Joule) as the user-centric metric, and for the network-centric counterpart, we consider a function of the sum of the throughputs of users in the network. The user-centric measure reflects the individual user's throughput, as well as the battery energy (transmit power) consumed to achieve it. The network-centric measure reflects the total revenue derived by the usage of network resources. We introduce an explicit pricing mechanism to mediate between the user-centric and network-centric resource-management problems. Users adjust their power in a distributed fashion to maximize the difference between their utilities and their payments (measured as a product of the unit price and throughput). The network adjusts the unit price in order to maximize its revenue (measured as the sum of the individual payments). We show that the distributed user-centric power control results in a unique Nash equilibrium. Our numerical results indicate that there exists a unique unit price that maximizes the revenue of the network. We also derive a semianalytical, computationally simple, and highly accurate approximation to the optimal solution. Our results show that while users with better channels receive better qualities of service, as usual (e.g., as in waterfilling), they also make proportionally higher contributions to the network revenue.     

Revenue optimization of B-ISDN networks

We consider the optimization of routing in a broadband ISDN network through the maximization of the revenue generated by the network. Two types of calls are offered to the network with different holding times and bandwidths. The routing is alternative routing and load sharing. First we state the Kuhn-Tucker conditions and show how they provide a simple routing rule based on equal marginal net revenue. We then proposed two-stage decomposition algorithm for optimizing the routing which requires a flow-deviation stage combined with the solution of a linear system of equations. We obtain simple expressions for the net path revenues and the corresponding optimal routing rule. We summarize numerical results related to the convergence of the algorithm and the structure of the optimal routing     

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.     

基于DiffServ的定价方法研究

本文针对IETF提出的DiffServ机制,提出了一个融合接入价格、使用价格、拥塞价格的多价格体系和基于用户效用的一个有效、实时的定价方法.该方法能对不同服务等级进行差异定价,对同一等级内的用户流量采用流量整形中的令牌桶方法进行拥塞计费,从而促使用户根据其实际应用需求合理选择服务等级;同时该方法能保证在突发流量时,用户可以根据价格调整发送速率或服务等级从而最大化其效用;另外,ISP可以根据当前资源分配情况,通过适当价格干预,引导各等级用户有序分布,形成一个以供需关系为主、兼具宏观调控的价格体系,使网络被高效利用.最后,模拟实验验证了该方法的公平性、有效性和灵活性.     

基于网络切片的网络效用最大化虚拟资源分配算法

为了实现网络资源的动态分配,提高网络资源利用率,满足用户业务多样性带来的切片网络差异需求,该文提出一种基于网络效用最大化的虚拟资源分配算法。该算法采用商业化模式将频谱资源作为收益载体,并对不同切片网络进行差异化定价。同时将计算资源和回程链路作为开销,还考虑了切片网络对计算资源和频谱资源的差异性需求,最后以最大化网络收益建立效用模型。并通过拉格朗日对偶分解设计了分布式迭代算法对效用模型进行求解。仿真结果表明,该算法提高了服务用户比例,并使得网络资源获得最大收益。     

Responsive pricing modeled with Stackelberg game for next-generation networks

In this paper, we study the problem of resource allocation between users in next-generation networks. The starting assumption is that the service provider tries to come up with convincing offers of service level to subscribers, based on demand responsive pricing scheme, while trying to maximize its profit. We developed two algorithms with different usage-based pricing strategies. Both algorithms solve pricing optimization problem using Stackelberg game with service provider acting as a leader and users behaving as followers. We developed the bandwidth management server to perform automatic optimal bandwidth allocation to each user's session and maximize its expected utility while maximizing the overall service provider's revenue. For both algorithms, we also performed the procedure for optimization of the capacity offered to users.     

State-Dependent Pricing and Its Economic Implications

In a packet-switched network, the service provider can charge users a state-dependent price, which depends on the extent to which the network is congested. Alternatively, one can charge users a long-term average price, which is set based on expected demand and capacity availability, but independent of instantaneous network conditions. In this paper, we compare the benefits of different pricing schemes from the service provider, society, and consumer perspectives. Our results suggest that adopting state-dependent pricing improves both profit and total benefits to the society, but may be detrimental to consumer benefits.     

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.     

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.     

An Efficient Cross-Layer Optimization Algorithm for Data Transmission in Wireless Sensor Networks

In this paper, we propose a cross layer congestion optimization scheme for allocating the resources of wireless sensor networks to achieve maximization of network performance. The congestion control, routing selection, link capacity allocation, and power consumption are all taken account to yield an optimal scheme based on the Lagrangian optimization. The Lagrangian multiplier is adopted to adjust power consumption, congestion rate, routing selection and link capacity allocation, so that the network performance can be satisfied between the trade-off of efficiency and fairness of resource allocation. The proposed algorithm can significantly achieve the maximization of network performance in relieving the network congestion with less power consumption. Excellent simulation results are obtained to demonstrate our innovative idea, and show the efficiency of our proposed algorithm.     

Optimal pricing in a free market wireless network

We consider an ad-hoc wireless network operating within a free market economic model. Users send data over a choice of paths, and scheduling and routing decisions are updated dynamically based on time varying channel conditions, user mobility, and current network prices charged by intermediate nodes. Each node sets its own price for relaying services, with the goal of earning revenue that exceeds its time average reception and transmission expenses. We first develop a greedy pricing strategy that maximizes social welfare while ensuring all participants make non-negative profit. We then construct a (non-greedy) policy that balances profits more evenly by optimizing a profit fairness metric. Both algorithms operate in a distributed manner and do not require knowledge of traffic rates or channel statistics. This work demonstrates that individuals can benefit from carrying wireless devices even if they are not interested in their own personal communication.     

基于非合作博弈的联合功率与速率控制算法

在多媒体CDMA网络中,需要对用户的功率进行控制,既保证每个用户的QoS要求,又不增加对其它用户的干扰,同时还要对用户的数据速率进行控制,以避免拥塞。将功率和速率控制等效为一个具有N个用户的非合作博弈,用效用函数表征用户对系统服务质量的满意程度,用代价函数描述达到通信要求所消耗的无线资源,从而将联合功率与速率控制算法描述为最大化净效用函数（效用函数和代价函数之差）的过程,最终得到系统中各用户全局最佳数据传输速率和达到服务质量所需的最小发射功率组合,并证明了纳什均衡的存在性。     

Control of wireless networks with flow level dynamics under constant time scheduling

We consider a network control problem for wireless networks with flow level dynamics under the general k-hop interference model. In particular, we investigate the control problem in low load and high load regimes. In the low load regime, we show that the network can be stabilized by a regulated maximal scheduling policy considering flow level dynamics if the offered load satisfies a constraining bound condition. Because maximal scheduling is a general scheduling rule whose implementation is not specified, we propose a constant-time and distributed scheduling algorithm for a general k-hop interference model which can approximate the maximal scheduling policy within an arbitrarily small error. Under the stability condition, we show how to calculate transmission rates for different user classes such that the long-term (time average) network utility is maximized. This long-term network utility captures the real network performance due to the fact that under flow level dynamics, the number of users randomly change so instantaneous network utility maximization does not result in useful network performance. Our results imply that congestion control is unnecessary when the offered load is low and optimal user rates can be determined to maximize users’ long-term satisfaction. In the high load regime where the network can be unstable under the regulated maximal scheduling policy, we propose a cross-layer congestion control and scheduling algorithm which can stabilize the network under arbitrary network load. Through extensive numerical analysis for some typical networks, we show that the proposed scheduling algorithm has much lower overhead than other existing queue-length-based constant-time scheduling schemes in the literature, and it achieves performance much better than the guaranteed bound.