Pricing computer services: queueing effects
Communications of the ACM
Optimal incentive-compatible priority pricing for the M/M/1 queue
Operations Research
Pricing in computer networks: motivation, formulation, and example
IEEE/ACM Transactions on Networking (TON)
Pricing in computer networks: reshaping the research agenda
ACM SIGCOMM Computer Communication Review
Responsive pricing in the Internet
Internet economics
Measurement-Based Usage Charges in Comminucations Networks
Operations Research
Priority Pricing in Utility Fair Networks
ICNP '05 Proceedings of the 13TH IEEE International Conference on Network Protocols
Load Regulation in Mobile Network with Planned Pricing Model based on User Behaviour
ICAS-ICNS '05 Proceedings of the Joint International Conference on Autonomic and Autonomous Systems and International Conference on Networking and Services
Pricing for fairness: distributed resource allocation for multiple objectives
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Pricing for QoS provisioning across multiple internet service provider domains
NET-COOP'07 Proceedings of the 1st EuroFGI international conference on Network control and optimization
Bandwidth allocation in networks: a single dual update subroutine for multiple objectives
CAAN'04 Proceedings of the First international conference on Combinatorial and Algorithmic Aspects of Networking
On tariffs, policing and admission control for multiservice networks
Operations Research Letters
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While equilibrium analysis has been commonly used for network pricing under the assumption that user utility functions are precisely known, many researchers have criticized the validity of the assumption. In this paper, we propose a solution for bridging the gap between the existing theoretical work on optimal pricing and the unavailability of precise user utility information in real networks. In the proposed method, the service provider obtains increasingly more accurate estimates of user utility functions by iteratively changing the prices of service levels and observing the users' service-level choices under various prices. Our study's contribution is twofold. First, we have developed a general principle for estimating user utility functions. Especially, we present the utility estimation for dynamic user population. Second, we have developed a method for setting prices that can optimize the extraction of information about user utility functions. The extensive simulation results demonstrate the effectiveness of our method.