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

Huijie Peng, Yan Cheng () and Xingyuan Li
Additional contact information
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

Sustainability, 2023, vol. 15, issue 4, 1-21

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.

Keywords: cloud computing; non-stationarity; real-time pricing; spot cloud service; reinforcement learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/4/3363/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/4/3363/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:4:p:3363-:d:1066081

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().