文献类型：期刊文献

英文题名：Real-Time Pricing Method for Spot Cloud Services with Non-Stationary Excess Capacity

作者：Peng, Huijie[1];Cheng, Yan[1];Li, Xingyuan[1]

机构：[1]East China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China

年份：2023

卷号：15

期号：4

外文期刊名：SUSTAINABILITY

收录：;WOS:【SSCI(收录号:WOS:000942092800001)，SCI-EXPANDED(收录号:WOS:000942092800001)】；

基金：This work was finished when Huijie Peng was a visiting student at the National University of Singapore. The support provided by the East China University of Science and Technology during the visit of Huijie Peng to the National University of Singapore is acknowledged.

语种：英文

外文关键词：cloud computing; non-stationarity; real-time pricing; spot cloud service; reinforcement learning

摘要：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.