# A New Dynamic Game-Based Pricing Model for Cloud Environment
## 2 Related Work  
In this section, a review of related works about pricing models in cloud computing environments is presented. Previous studies, including [4; 8], have presented and contrasted various pricing techniques within the domain of cloud computing. Auction-based pricing, also known as dynamic pricing, is a widely used pricing mechanism for cloud services [9; 10; 11; 12]. In [13], the authors proposed the online extended consensus revenue estimate mechanism for conducting a recurrent, multiunit, and single-price auction in the Infrastructure as a Service (IaaS) cloud resource setting. This mechanism is both envy-free and truthful, and it is capable of generating profits that are close to optimal. It incorporates a system for dynamically determining reserve prices based on the Power Usage Effectiveness (PUE) of the data center and the costs of electricity. Through the use of simulation-based assessment, its efficacy has been proven across a range of market conditions, surpassing the conventional uniform price auction. The authors also investigated the importance of prior knowledge in maximizing profit by considering the execution time of virtual machines. A small-scale experimental study involving ten users has confirmed the truthfulness of this mechanism in a real-world testing environment. In [9], a double-auction pricing model named Combinatorial Double Auction Resource Allocation (CDARA) was proposed to balance profit between user and provider. In [10], the CDARA model has been improved. In order to achieve a higher level of fairness, this model takes into account not only the price but also other parameters related to the quality of service during the resource allocation process. If the provider does not meet the required service quality, it imposes penalties on providers and diminishes their reputation. In [11], the resource allocation problem was formulated as a linear programming model to maximize the total profit of users and providers using a combinatorial double auction-based pricing mechanism. In [12], a QoS-based auction method was proposed, which attempts to dynamically determine the price based on the quality of service parameters (such as availability) to increase the provider's profit. Performance-based pricing is another approach in which cloud providers are paid based on the actual consumption of their resources. For example, [PERSON] et al. proposed a pricing model according to the CPU capacity consumed by the user application [14]. They considered the number of CPU cores and the selected CPU frequency in the pricing model. Similarly, another study conducted by [PERSON] et al. put forth a pricing policy that takes into account the quantity of energy consumed as well as the real usage of resources per unit, including memory, network, and disk. For this purpose, a mathematical approach has been used to calculate energy consumption and resource usage [15]. The distinction between customers and using incentive mechanisms has always been the focus of pricing methods. For example, [PERSON] et al. proposed a fairness-aware pricing model to increase the profitability of cloud infrastructure providers [16]. They offered lower prices to the customers whose requested resources could be allocated efficiently to encourage them to use cloud services more. Using algorithms from financial option theory, [PERSON] et al. proposed a pricing architecture for cloud services. A commonly used equation, known as the compound-Moore's law, was put forward to calculate the cost using factors like the properties of resources, the rate of inflation, and the decline in value. They also used a genetic algorithm and fuzzy logic-based approach to measure the potential loss of cloud providers [17]. The law of supply and demand in marketing is another basis for determining cloud service prices. [PERSON] et al. proposed a dynamic model that determines the price for cloud services according to the real supply-and-demand relationship.