The rapid development of Internet of Things (IoT) technology and the popularity of Artificial Intelligence (Al) technology research have brought new opportunities for the development of cloud computing (CC). With the increasing number of mobile Internet access devices and IoT access devices, the number of task requests from CC customers for AI services in the network has also experienced an explosive growth. In this paper, the focus is on the possible overload of cloud providers during the peak period of cloud service requests. Thetime attributes of cloud task execution are classified to avoid overloading the cloud provider as much as possible. In a distributed cloud environment, it is necessary to consider the time flexible attributes of cloud tasks to reasonably compete for cloud resources. In this work, game theory (GT) is introduced to formulate a cloud service scheduling game, in which participants are cloud customers who participate in the purchase of cloud services. The players' strategies are the time flexibility of each cloud task. The problem is formulated as minimizing the cost of scheduling cloud services and a noncooperative game among the customers (as players) is presented. Then the existence of the Nash equilibrium (NE) solution of the game has been proved and a new algorithm has been proposed in this paper to compute it. In addition, the analysis process of the convergence of the proposed PCA algorithm and the proof of its convergence to NE are also included in this paper. At the end of the paper, simulations were performed to verify the theoretical analysis presented. The experimental results show that the proposed PCA algorithm can converge to the Nash equilibrium very quickly, effectively reducing the peak value and increasing profit.
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