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Volume 34 Issue 3
May  2019
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Jiang Rong, Tao Qin, Bo An. Competitive Cloud Pricing for Long-Term Revenue Maximization[J]. Journal of Computer Science and Technology, 2019, 34(3): 645-656. DOI: 10.1007/s11390-019-1933-9
Citation: Jiang Rong, Tao Qin, Bo An. Competitive Cloud Pricing for Long-Term Revenue Maximization[J]. Journal of Computer Science and Technology, 2019, 34(3): 645-656. DOI: 10.1007/s11390-019-1933-9
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Competitive Cloud Pricing for Long-Term Revenue Maximization

  • 1 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China;
    3 Microsoft Research Asia, Beijing 100080, China;
    4 School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore

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  • Author Bio:

    Jiang Rong is a Ph.D. candidate of Institute of Computing Technology, Chinese Academy of Sciences, Beijing. His research interests include deep learning, reinforcement learning, artificial intelligence, game theory and multi-agent systems. He got his B.S. degree in information engineering from South China University of Technology, Guangzhou, in 2013.

  • Received Date: May 08, 2018
  • Revised Date: March 22, 2019
  • Published Date: May 04, 2019
    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.
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    • References (32)
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