Scheduling policies for an on-demand video server with batching
MULTIMEDIA '94 Proceedings of the second ACM international conference on Multimedia
Pricing considerations in video-on-demand systems (poster session)
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Prospects for Interactive Video-on-Demand
IEEE MultiMedia
Using Tree Topology for Multicast Congestion Control
ICPP '02 Proceedings of the 2001 International Conference on Parallel Processing
A model for discovering customer value for E-content
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Price issues in delivering E-content on-demand
ACM SIGecom Exchanges
Pricing and resource provisioning for delivering E-content on-demand with multiple levels-of-service
QofIS'02/ICQT'02 Proceedings of the 3rd international conference on quality of future internet services and internet charging and QoS technologies 2nd international conference on From QoS provisioning to QoS charging
Client-driven price selection for scalable video streaming with advertisements
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
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Content delivery is a growing enterprise in the Internet. Critical to the management of content delivery systems is understanding customer behavior, its impact on the consumption of system resources and how together, these affect revenue. We believe that price dictates overall customer behavior and that understanding the relationship between price and customer behavior is the key to effective system management. To this end, we study the impact of price on revenue and system utilization. Our service model is based on customers being able to refuse content basedon their capacity to pay and their willingness to pay the price quoted. We quantify the effects of such customer behavior on revenue andsystem utilization. Since customer behavior and characteristics are highly varying and not known a priori, we develop an adaptive pricing model which tracks user behavior as well as the arrival process to maximize revenue. We validate it using simulation. Our simulation results indicate that the adaptive pricing scheme generates nearly the same revenue as the theoretical expectation under very dynamic workloads.