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Regular Paper

Competitive Cloud Pricing for Long-Term Revenue Maximization

Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
Microsoft Research Asia, Beijing 100080, China
School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
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Abstract

We study the pricing policy optimization problem for cloud providers while considering three properties of the real-world market: 1) providers have only incomplete information about the market; 2) it is in evolution due to the increasing number of users and decreasing marginal cost of providers; 3) it is fully competitive because of providers’ and users’ revenuedriven nature. As far as we know, there is no existing work investigating the optimal pricing policies under such realistic settings. We first propose a comprehensive model for the real-world cloud market and formulate it as a stochastic game. Then we use the Markov perfect equilibrium (MPE) to describe providers’ optimal policies. Next we decompose the problem of computing the MPE into two subtasks: 1) dividing the stochastic game into many normal-formal games and calculating their Nash equilibria, for which we develop an algorithm ensuring to converge, and 2) computing the MPE of the original game, which is efficiently solved by an algorithm combining the Nash equilibria based on a mild assumption. Experimental results show that our algorithms are efficient for computing MPE and the MPE strategy leads to much higher profits for providers compared with existing policies.

Keywords

cloud computinggame theoryMarkov perfect equilibriumrevenue maximization

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Journal of Computer Science and Technology
Volume 34 Issue 3,
May 2019
Pages 645-656
DOI: 10.1007/s11390-019-1933-9
Cite this article:
Rong J, Qin T, An B. Competitive Cloud Pricing for Long-Term Revenue Maximization. Journal of Computer Science and Technology, 2019, 34(3): 645-656. https://doi.org/10.1007/s11390-019-1933-9

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Received: 09 May 2018
Revised: 23 March 2019
Published: 10 May 2019
©2019 Springer Science + Business Media, LLC & Science Press, China
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