Mobile data traffic has been steadily rising in the past years. This has generated a significant interest in the deployment of incentive mechanisms to reduce peak-time congestion. Typically, the design of these mechanisms requires information about user demand and sensitivity to prices. Such information is naturally imperfect. In this paper, we propose a fixed-budget rebate mechanism that gives each user a reward proportional to his percentage contribution to the aggregate reduction in peak-time demand. For comparison, we also study a time-of-day pricing mechanism that gives each user a fixed reward per unit reduction of his peak-time demand. To evaluate the two mechanisms, we introduce a game-theoretic model that captures the public good nature of decongestion. For each mechanism, we demonstrate that the socially optimal level of decongestion is achievable for a specific choice of the mechanism's parameter. We then investigate how imperfect information about user demand affects the mechanisms' effectiveness. From our results, the fixed-budget rebate pricing is more robust when the users' sensitivity to congestion is "sufficiently" convex. This feature of the fixed-budget rebate mechanism is attractive for many situations of interest and is driven by its closed-loop property, i.e., the unit reward decreases as the peak-time demand decreases.
Many pricing mechanisms have been proposed to manage quality of services (QoS) in networks, see e.g., surveys @cite_28 @cite_2 @cite_22 . For instance, in @cite_33 , Honig and Steiglitz propose a usage-based pricing mechanism, and analyze it using a model with delay-sensitive users. Their results show how to find the price that maximizes the provider's revenue by solving a fixed-point equation. A similar model is used in @cite_32 where Ba s ar and Srikant analyze the many-users limit. They show that, as the number of users increases, the provider's revenue per unit of bandwidth increases and conclude that this gives providers an incentive to increase their network capacity. In a number of papers, e.g., @cite_13 @cite_6 @cite_21 , pricing mechanisms based on multiple classes of customers with different priorities are proposed and analyzed in terms of equilibrium achieved and optimal price per class. In @cite_17 @cite_7 , Shen and Ba s ar investigate the performance of non-linear pricing in a model similar to @cite_32 and find an improvement of up to 38 contrast, in this paper, we investigate linear pricing mechanisms that leverage the time variability of user demand using a single priority class.
@cite_22 @cite_33 @cite_7 @cite_28 @cite_21 @cite_32 @cite_6 @cite_2 @cite_13 @cite_17
2099797210 2953153691 2143535483 2100853709 2264445763 2133471947 2951024449 2136340918 1985441584 2018061752
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. We present a new stochastic service model with capacity sharing and interruptions, appropriate for the evaluation of the quality of real-time streaming (RTS), like e.g. mobile TV, in wireless cellular networks. The general model takes into account multi-class Markovian process of call arrivals, (to capture different radio channel conditions, requested streaming bit-rates and durations) and allows for a general resource allocation policy saying which users are temporarily denied the requested fixed streaming bit-rates (put in outage) due to resource constraints. We give expressions for several important performance characteristics of the model, including mean time spent in outage and mean number of outage incidents for a typical user of a given class. These expressions involve only stationary probabilities of the (free) traffic demand process, which is a vector of independent Poisson random variables describing the number of users of different classes. In order to analyze RTS in 3GPP Long Term Evolution (LTE) cellular networks, we specify our general model assuming orthogonal user channels with the peak bit-rates close to the theoretical Shannon's bound in the additive white Gaussian noise (AWGN) channel, which leads to the resource constraints in a multi-rate linear form. In this setting we consider a natural class of least-effort-served-first resource allocation policies, for which the characteristics of the model can be further evaluated using Fourier analysis of Poisson variables. Within this class we identify and evaluate an optimal and a fair policy, the latter being suggested by LTE implementations. We also propose some intermediate policies, which allow to solve the optimality fairness tradeoff caused by unequal user radio-channel conditions. Our results can be used for the evaluation of the quality of RTS in LTE networks and dimensioning of these networks. We address the problem of spectrum pricing in a cognitive radio network where multiple primary service providers compete with each other to offer spectrum access opportunities to the secondary users. By using an equilibrium pricing scheme, each of the primary service providers aims to maximize its profit under quality of service (QoS) constraint for primary users. We formulate this situation as an oligopoly market consisting of a few firms and a consumer. The QoS degradation of the primary services is considered as the cost in offering spectrum access to the secondary users. For the secondary users, we adopt a utility function to obtain the demand function. With a Bertrand game model, we analyze the impacts of several system parameters such as spectrum substitutability and channel quality on the Nash equilibrium (i.e., equilibrium pricing adopted by the primary services). We present distributed algorithms to obtain the solution for this dynamic game. The stability of the proposed dynamic game algorithms in terms of convergence to the Nash equilibrium is studied. However, the Nash equilibrium is not efficient in the sense that the total profit of the primary service providers is not maximized. An optimal solution to gain the highest total profit can be obtained. A collusion can be established among the primary services so that they gain higher profit than that for the Nash equilibrium. However, since one or more of the primary service providers may deviate from the optimal solution, a punishment mechanism may be applied to the deviating primary service provider. A repeated game among primary service providers is formulated to show that the collusion can be maintained if all of the primary service providers are aware of this punishment mechanism, and therefore, properly weight their profits to be obtained in the future. In the communication network pricing literature, it is the linear pricing schemes that have been largely adopted as the means of controlling network usage or generating profits for network service providers. This paper extends the framework to nonlinear pricing and investigates optimal nonlinear pricing policy design for a monopolistic service provider. The problem is formulated as an incentive-design problem, and incentive (pricing) policies are obtained for a many-users regime, which enable the service provider to approach arbitrarily close to Pareto- optimal solutions. Under the assumption that the service provider knows the true user types, analytical and numerical results indicate a profit improvement exceeding 38 over linear pricing by the introduction of nonlinear pricing. We also consider the scenario where the service provider has incomplete information on user types. A comparative study of the results for complete information and incomplete information is carried out as well, with numerical results pointing to 25 -40 loss of profit by the service provider due to incompleteness of information on the user types. Dynamic forms of resource pricing have recently been introduced by cloud providers that offer Infrastructure as a Service (IaaS) capabilities in order to maximize profits and balance resource supply and demand. The design of a mechanism that efficiently prices perishable cloud resources in line with a provider’s profit maximization goal remains an open research challenge, however. In this article, we propose the Online Extended Consensus Revenue Estimate mechanism in the setting of a recurrent, multiunit and single price auction for IaaS cloud resources. The mechanism is envy-free, has a high probability of being truthful, and generates a near optimal profit for the provider. We combine the proposed auction design with a scheme for dynamically calculating reserve prices based on data center Power Usage Effectiveness (PUE) and electricity costs. Our simulation-based evaluation of the mechanism demonstrates its effectiveness under a broad variety of market conditions. In particular, we show how it improves on the classical uniform price auction, and we investigate the value of prior knowledge on the execution time of virtual machines for maximizing profit. We also developed a system prototype and conducted a small-scale experimental study with a group of 10 users that confirms the truthfulness property of the mechanism in a real test environment. This paper considers the problem of pricing and transmission scheduling for an access point (AP) in a wireless network, where the AP provides service to a set of mobile users. The goal of the AP is to maximize its own time-average profit. We first obtain the optimum time-average profit of the AP and prove the "Optimality of Two Prices" theorem. We then develop an online scheme that jointly solves the pricing and transmission scheduling problem in a dynamic environment. The scheme uses an admission price and a business decision as tools to regulate the incoming traffic and to maximize revenue. We show the scheme can achieve any average profit that is arbitrarily close to the optimum, with a tradeoff in average delay. This holds for general Markovian dynamics for channel and user state variation, and does not require a priori knowledge of the Markov model. The model and methodology developed in this paper are general and apply to other stochastic settings where a single party tries to maximize its time-average profit. This paper studies the problem of utility maximization for clients with delay based QoS requirements in wireless networks. We adopt a model used in a previous work that characterizes the QoS requirements of clients by their delay constraints, channel reliabilities, and delivery ratio requirements. In this work, we assume that the utility of a client is a function of the delivery ratio it obtains. We treat the delivery ratio for a client as a tunable parameter by the access point (AP), instead of a given value as in the previous work. We then study how the AP should assign delivery ratios to clients so that the total utility of all clients is maximized. We apply the techniques introduced in two previous papers to decompose the utility maximization problem into two simpler problems, a CLIENT problem and an ACCESS-POINT problem. We show that this decomposition actually describes a bidding game, where clients bid for the service time from the AP. We prove that although all clients behave selfishly in this game, the resulting equilibrium point of the game maximizes the total utility. In addition, we also establish an efficient scheduling policy for the AP to reach the optimal point of the ACCESS-POINT problem. We prove that the policy not only approaches the optimal point but also achieves some forms of fairness among clients. Finally, simulation results show that our proposed policy does achieve higher utility than all other compared policies. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users. Providing differentiated Quality of Service (QoS) over unreliable wireless channels is an important challenge for supporting several future applications. We analyze a model that has been proposed to describe the QoS requirements by four criteria: traffic pattern, channel reliability, delay bound, and throughput bound. We study this mathematical model and extend it to handle variable bit rate applications. We then obtain a sharp characterization of schedulability vis-a-vis latencies and timely throughput. Our results extend the results so that they are general enough to be applied on a wide range of wireless applications, including MPEG Variable-Bit-Rate (VBR) video streaming, VoIP with differentiated quality, and wireless sensor networks (WSN). Two major issues concerning QoS over wireless are admission control and scheduling. Based on the model incorporating the QoS criteria, we analytically derive a necessary and sufficient condition for a set of variable bit-rate clients to be feasible. Admission control is reduced to evaluating the necessary and sufficient condition. We further analyze two scheduling policies that have been proposed, and show that they are both optimal in the sense that they can fulfill every set of clients that is feasible by some scheduling algorithms. The policies are easily implemented on the IEEE 802.11 standard. Simulation results under various settings support the theoretical study. This paper presents a comprehensive analytical study of two competitive secondary operators' investment (i.e., spectrum leasing) and pricing strategies, taking into account operators' heterogeneity in leasing costs and users' heterogeneity in transmission power and channel conditions. We model the interactions between operators and users as a three-stage dynamic game, where operators simultaneously make spectrum leasing decisions in Stage I, and pricing decisions in Stage II, and then users make purchase decisions in Stage III. Using backward induction, we are able to completely characterize the dynamic game's equilibria. We show that both operators' investment and pricing equilibrium decisions process interesting threshold properties. For example, when the two operators' leasing costs are close, both operators will lease positive spectrum. Otherwise, one operator will choose not to lease and the other operator becomes the monopolist. For pricing, a positive pure strategy equilibrium exists only when the total spectrum investment of both operators is less than a threshold. Moreover, two operators always choose the same equilibrium price despite their heterogeneity in leasing costs. Each user fairly achieves the same service quality in terms of signal-to-noise ratio (SNR) at the equilibrium, and the obtained predictable payoff is linear in its transmission power and channel gain. We also compare the duopoly equilibrium with the coordinated case where two operators cooperate to maximize their total profit. We show that the maximum loss of total profit due to operators' competition is no larger than 25 percent. The users, however, always benefit from operators' competition in terms of their payoffs. We show that most of these insights are robust in the general SNR regime.