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Real-Time Pricing Method for Spot Cloud Services with Non-Stationary Excess Capacity

Author

Listed:
  • Huijie Peng

    (School of Business, East China University of Science and Technology, Shanghai 200237, China)

  • Yan Cheng

    (School of Business, East China University of Science and Technology, Shanghai 200237, China)

  • Xingyuan Li

    (School of Business, East China University of Science and Technology, Shanghai 200237, China)

Abstract

Cloud operators face massive unused excess computing capacity with a stochastic non-stationary nature due to time-varying resource utilization with peaks and troughs. Low-priority spot (pre-emptive) cloud services with real-time pricing have been launched by many cloud operators, which allow them to maximize excess capacity revenue while keeping the right to reclaim capacities when resource scarcity occurs. However, real-time spot pricing with the non-stationarity of excess capacity has two challenges: (1) it faces incomplete peak–trough and pattern shifts in excess capacity, and (2) it suffers time and space inefficiency in optimal spot pricing policy, which needs to search over the large space of history-dependent policies in a non-stationary state. Our objective was to develop a real-time pricing method with a spot pricing scheme to maximize expected cumulative revenue under a non-stationary state. We first formulated the real-time spot pricing problem as a non-stationary Markov decision process. We then developed an improved reinforcement learning algorithm to obtain the optimal solution for real-time pricing problems. Our simulation experiments demonstrate that the profitability of the proposed reinforcement learning algorithm outperforms that of existing solutions. Our study provides both efficient optimization algorithms and valuable insights into cloud operators’ excess capacity management practices.

Suggested Citation

  • Huijie Peng & Yan Cheng & Xingyuan Li, 2023. "Real-Time Pricing Method for Spot Cloud Services with Non-Stationary Excess Capacity," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3363-:d:1066081
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    References listed on IDEAS

    as
    1. Rana, Rupal & Oliveira, Fernando S., 2014. "Real-time dynamic pricing in a non-stationary environment using model-free reinforcement learning," Omega, Elsevier, vol. 47(C), pages 116-126.
    2. Yanzhe (Murray) Lei & Stefanus Jasin, 2020. "Real-Time Dynamic Pricing for Revenue Management with Reusable Resources, Advance Reservation, and Deterministic Service Time Requirements," Operations Research, INFORMS, vol. 68(3), pages 676-685, May.
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    Cited by:

    1. Bouchra El Akraoui & Daoui Cherki, 2023. "Solving Finite-Horizon Discounted Non-Stationary MDPS," Folia Oeconomica Stetinensia, Sciendo, vol. 23(1), pages 1-15, June.

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