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.
@cite_3: We propose Trade & Cap (T&C), an economics-inspired mechanism that incentivizes users to voluntarily coordinate their consumption of the bandwidth of a shared network link so as to converge on what they perceive to be an equitable allocation, while ensuring efficient resource utilization. Under T&C, rather than acting as an arbiter, a service provider acts as an enforcer of what the community of rational users sharing the resource decides is a fair allocation of that resource. Our T&C mechanism proceeds in two phases. In the first, software agents acting on behalf of users engage in a strategic trading game in which each agent selfishly chooses reserved bandwidth slots to acquire in support of primary network usage activities. In the second phase, these agents acquire additional bandwidth slots in support of a presumed open-ended need for fluid bandwidth, catering to secondary applications. The acquisition of this fluid bandwidth is subject to the remaining "buying power" of each user and by prevalent "market prices" - both of which are determined by the outcomes of the trading phase, and by a desirable aggregate cap on link utilization. We present analytical results that establish the underpinnings of our T&C mechanism, including game-theoretic results pertaining to the trading phase, and pricing of fluid bandwidth allocation pertaining to the capping phase. Using Internet traffic traces, our experimental results demonstrate the benefits of our scheme, which we also show to be practical by highlighting the salient features of an efficient implementation architecture.
@cite_11: 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.
A few papers have proposed mechanisms with prices dependent on congestion levels. In @cite_11 , Paschalidis and Tsitsiklis propose a congestion-based pricing mechanism in the context of loss networks (i.e., phone). They provide a dynamic programming formulation of the revenue maximization problem and of the welfare maximization problem. Then, they show that this dynamic congestion pricing mechanism can be well approximated by a simpler static time-of-day pricing. An alternative mechanism called Trade & Cap'', was recently proposed by Londo n o, Bestavros and Laoutaris @cite_3 . It works in two phases. First, users engage in a trading game where they choose an amount of bandwidth slots to buy for hard-constraints traffic. In the second phase, the remaining bandwidth is allocated to users as bandwidth, in proportion of their remaining buying power''. They show that this mechanism smoothes the aggregate demand to a certain level. In their model, users have a cost function that increases linearly with the total demand in a given slot. In this paper, we consider simpler one-phase pricing mechanisms with fixed parameters. Our model also differ from these papers in that users have elastic demand and their utility is an arbitrary function of the congestion level.