# A New Dynamic Game-Based Pricing Model for Cloud Environment
## 2 Related Work
They employed closed-loop control mechanisms to dynamically modify the price for cloud services. They also considered penalties for SLA violations by the provider and rewards for users who are not sensitive to delay [5]. Similarly, in [18], the authors also proposed a value-based pricingsolution that uses a hedonic regression model to estimate how much a customer is willing to pay for the cloud service. The amount of energy consumed is another important factor that has been considered recently in pricing methods for achieving a fairer price and making computation greener [15; 19]. For instance, [PERSON] et al. have introduced a pricing solution based on greediness in order to decrease energy usage in cloud computing and transition towards environmentally friendly cloud computing [19]. They showed that the price of resources should be proportional to their energy consumption. Several dynamic pricing models have been conducted based on game theory [20; 21; 22; 23; 24; 25]. In [20], the authors suggested a framework for the cloud computing market to make the market uniform and competitive, whereby users can purchase resources from different companies and flexibly exchange their idle resources. Then, a static game-based pricing method is proposed to mathematically model the pricing problem and develop an optimal strategy for the user and cloud provider. In a study of price competition in a diverse cloud market, the authors examined the rivalry between cloud service providers (CSPs) and cloud users [21]. They devised a two-stage non-cooperative game, where CSPs established prices to maximize their revenue in the first stage. By using an iterative algorithm, they obtained Nash equilibrium prices and analyzed the convergence properties. In the second stage, cloud users opted for services based on performance and price, employing an evolutionary game approach. The result demonstrated that the rate of arrival or resource capacity had a more substantial impact on the CSP side, influencing equilibrium prices and utilities, as opposed to the cloud user side, which affected user cost and arrival rates. In [26], a framework was introduced that acts as an intermediary between multiple cloud platforms to facilitate the processing of large-scale data streams. This framework offers cloud services for streaming big data processing to users. The process includes renting computer resources from various cloud services and providing users with different service interfaces. To increase income and reduce potential harm, a strategy called Pricing-Repurchasing is created. This strategy involves forming long-term rental agreements with customers. The Pricing-Repurchasing problem was mathematically formulated as a two-stage leader-follower game (Stackelberg), and the equilibrium was thoroughly analyzed. The simulations were conducted to evaluate the effectiveness of the pricing strategy, demonstrating that it generates more revenue for the intermediary than other approaches. The authors in [22] formulated the problem of setting prices for cloud web services with a collaborative game theory approach. They concluded that cloud platforms can co-exist if they adopt the same pricing strategy. In [27], the authors explored the issue of maximizing revenue in the context of SaaS and IaaS providers. They depicted this issue by viewing it as a Stackelberg game and examining the presence and uniqueness of the game equilibrium. Furthermore, examining the influence of resource prices on the users' desire to utilize the service was conducted, and a dynamic pricing strategy was proposed to maximize the revenue for both SaaS and IaaS providers. By performing simulations, the results demonstrate that the proposed strategy surpasses fixed pricing and auction-based pricing mechanisms in terms of revenue maximization and resource utilization. Ref [28] proposed a pricing strategy that aims to achieve fairness for cloudlet resources.