# A New Dynamic Game-Based Pricing Model for Cloud Environment
## 2 Related Work
The goal is to find a middle ground that satisfies the needs and desires of both Cloudlet Infrastructure Providers (CIPs) and maximizes overall profits [28]. They introduced a model based on game theory that considers the initial quotes, idle resource rates of CIPs, and the overall profit of the Cloudlet Federation (CLF). The strategy for adjusting prices takes into consideration demand-side psychology and the rate at which the game process converges. The research demonstrates the existence of a solution that achieves Nash equilibrium and introduces an algorithm, known as the game pricing approach based on selection times, to solve the model. The results indicate significant improvements in total profit (more than 20%) and resource utilization (over 50%) than existing methods. A study in cloud manufacturing examined three resource-sharing strategies: independence, alliance, and co-operation with a cloud platform operator [29]. The research examined how these strategies affected meeting client needs. The findings, represented as a two-stage Stackelberg game, showed that when suppliers formed an alliance, the system profit decreased compared to when they shared independently. This contrasted with the platform operator's preferences. The analysis also highlighted the complexities involved in task and profit allocation when multiple suppliers were involved. Suppliers with lower prices had the advantage in tasks and profits based on varying marginal prices. These findings guide suppliers in choosing appropriate resource-sharing strategies in cloud manufacturing. In [30], a hybrid approach was used to optimize fair prices of resources through a stock market-based technical analysis and maximize resource utilization using Stackelberg output volume in a cloud service provider pool. The experiment incorporates three price rebalancing methods: exponential moving average, pivot point analysis, and relative strength index. The proposed approach ensures higher revenues and improved utilization rates for service providers while subscribers benefit from fair prices and enhanced resource availability. Overall, this solution offers advantages to both service providers and subscribers as it optimizes revenue, enhances resource utilization, and guarantees equitable pricing and resource availability. As mentioned above, cloud pricing policies can be categorized as fixed and dynamic. In selected methods, the price cannot be well adapted based on the supply and demand of the cloud market. Therefore, lower economic profit and customer satisfaction are expected for fixed pricing strategies compared to dynamic ones. Therefore, the purpose of this paper is to introduce a pricing method based on dynamic game theory. This method aims to enhance the fairness of the estimated price and simultaneously maximize the profit of the provider and the satisfaction of the customer. The proposed method is modeled using a dynamic game framework. The game is defined between the provider and the user. The effect of other cloud providers and market conditions is seen as the relation of supply and demand in the proposed method. The proposed approach takes into account a wide range of factors that affect the price, including energy consumption, the quantity and duration of resource usage, depreciation, and maintenance expenses, in order to achieve a price that is as fair as possible. Furthermore, virtual machines' performance interference effects and failure probability of cloud resources are incorporated into the proposed pricing method as two new influencing factors.