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Dynamic Cloud Pricing for Revenue Maximization
July-December 2013 (vol. 1 no. 2)
pp. 158-171
Hong Xu, City University of Hong Kong, Hong Kong
Baochun Li, University of Toronto, Toronto
In cloud computing, a provider leases its computing resources in the form of virtual machines to users, and a price is charged for the period they are used. Though static pricing is the dominant pricing strategy in today's market, intuitively price ought to be dynamically updated to improve revenue. The fundamental challenge is to design an optimal dynamic pricing policy, with the presence of stochastic demand and perishable resources, so that the expected long-term revenue is maximized. In this paper, we make three contributions in addressing this question. First, we conduct an empirical study of the spot price history of Amazon, and find that surprisingly, the spot price is unlikely to be set according to market demand. This has important implications on understanding the current market, and motivates us to develop and analyze market-driven dynamic pricing mechanisms. Second, we adopt a revenue management framework from economics, and formulate the revenue maximization problem with dynamic pricing as a stochastic dynamic program. We characterize its optimality conditions, and prove important structural results. Finally, we extend to consider a nonhomogeneous demand model.
Index Terms:
Pricing,Stochastic processes,Cost accounting,Numerical models,Analytical models,Cloud computing,dynamic programming,Dynamic pricing,revenue maximization,spot market,cloud computing,public cloud
Citation:
Hong Xu, Baochun Li, "Dynamic Cloud Pricing for Revenue Maximization," IEEE Transactions on Cloud Computing, vol. 1, no. 2, pp. 158-171, July-Dec. 2013, doi:10.1109/TCC.2013.15
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