A Many-to-Many Demand and Response Hybrid Game Method for Cloud Environments


In this piece, we will design a service mechanism for optimizing profits between multiple cloud providers and multiple cloud customers (also known as "many-to-many"). We investigate this issue from the point of view of game theory, but take a different approach compared to other cloud resource pricing game methods already in existence. To begin, we take the perspective that the interactions that occur between multiple cloud customers are a form of game and formulate the rivalries that occur between multiple cloud providers as a form of game that does not involve cooperation. In the end, we will have formed a hybrid game model in which not only the strategy of each customer and each cloud provider is affected by the other side, but also the strategies of customers and cloud providers who are not themselves involved. Second, using the hybrid game model, we simulate the negotiation process that takes place between cloud service providers and customers. We do this by controlling the allocation of supply and demand, and our end goal is to achieve a state in which supply and demand are balanced, also known as a win-win situation. We come up with a utility function for each cloud customer and provider individually. The utility of a customer takes into account both.Net profits and the bidding strategies of cloud providers, while the utility of a cloud provider takes into account both.Net profits and the demand strategies of cloud customers. Under the influence of one another, both camps work to boost their individual profit margins as much as possible. We present evidence that our proposed strategies make it possible for both of the games to arrive at their respective equilibrium states. An iterative proximal algorithm, also known as an IPA, and a distributed iterative algorithm, also known as a DIA, are two types of algorithms that can be used to put cloud customers' and providers' strategies into action. Our methods have been validated by the experimental results, which also demonstrate that the proposed method can be of use to multiple cloud providers as well as customers.

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