Incentive Mechanisms for Internet Congestion Management: Fixed-Budget Rebate Versus Time-of-Day Pricing

Abstract

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-ofday 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 fixedbudget rebate pricing is more robust when the users' sensitivity to congestion is "sufficiently" convex. This feature of the fixedbudget 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. Index Terms-congestion pricing; lottery-based incentive mechanisms; public good provisioning; probabilistic pricingPatrick Loiseau is with EURECOM,

Pricing data center demand response

“…In particular, in this paper we have adopted quadratic objectives, and it is important to understand the impact of this. For example, in the context of internet congestion management, [36] has studied the impact of convexity of costs on the contrast between time-ofuse pricing and fixed-budget rebates. A similar study in the context of predictive pricing and supply function bidding is crucial.…”

Incentive Mechanisms for Crowdsensing: Crowdsourcing With Smartphones

“…We assume that and is a strictly concave function in variables for any fixed and also monotonically increasing in each for each . This assumption is realistic and general, which has been adopted in the literature [24], [25].…”

“…As a notational convention, we write . Note that this incentive mechanism is similar to the lottery-based mechanisms in [24], [25]. The difference lies in the assumption that a large number of users are considered and thus each user's strategy is negligible comparing to the total strategy.…”

“…The difference lies in the assumption that a large number of users are considered and thus each user's strategy is negligible comparing to the total strategy. In addition, the objective in [24], [25] is to choose the mechanism parameters to achieve the socially optimal level. While in our model, the crowdsourcer is only interested in maximizing its own utility.…”

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“…Flat-rate pricing was widely adopted because it was easy for ISPs to implement and was preferred by users for its simplicity. However, with the growing popularity of data intensity services, e.g., online video streaming and cloud-based applications, researchers have pro posed many advanced pricing mechanisms, e.g., congestion based [2,3], time-constrained [4][5][6] and usage-based [7][8][9] pricing, to improve revenues or control congestions for ISPs. In this work, we consider the widely used two-part tariffs, where additional charges are imposed if a user's data demand exceeds a predefined data cap and the exceeded amount is charged based on a per-unit fee.…”