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Top1. Introduction
With the continuous development of cloud computing, a variety of cloud-based network-intensive applications are emerging one after another, and gradually penetrate into people's lives. As we all know, network-intensive applications usually consume a lot of network resources when they are running, so how to ensure the quality of service when these applications are running is an important issue facing cloud computing. Previous discussions on cloud data center mapping have often focused on maximizing revenue, but ignoring the overhead of electricity has gradually become the main cost of cloud data center operations. With the unprecedented development of cloud data centers, reducing energy consumption has become a major challenge for cloud service providers. Virtual data center mapping requires multiple steps and is a complex task. If it is not effectively planned and deployed, it will cause great energy loss.
Cloud computing, as a new model for the allocation of Internet resources, implements a service model that is allocated on demand. The powerful data processing capabilities in the cloud solve the problem of low computing performance due to insufficient resources for cloud computing users (Wang et al., 2016). In view of the cloud computing service model, the cloud server and cloud clients are generally one-to-many or many-to-many models. When connecting and communicating with multiple users, it is necessary to guarantee the quality of data communication services on the one hand, and Considering the energy consumption of communication, especially when the number of potential users of mobile devices increases, the energy consumption problem of the mobile terminal becomes the short board of the mobile device, so it is very important to solve the energy consumption problem of the cloud computing environment (Patra et al., 2016). How to ensure the balanced development of computing performance, service quality, and energy consumption of cloud computing is a key issue that needs to be solved in the current cloud computing environment (Mehdinejad et al., 2017). Based on this problem, this paper develops an energy efficiency optimization algorithm based on fuzzy decoupling. Prior to this, some scholars have made some achievements in cloud computing energy efficiency research. Although these studies have achieved certain results in the process of energy efficiency index optimization, there has been little research on the extraction of energy efficiency index parameters, resulting in energy efficiency. During the optimization process, there is no emphasis on the optimization of the index parameters (Hsu et al., 2016). In this paper, the fuzzy decoupling method commonly used in the industrial field is used to extract energy efficiency parameters and evaluate key factors to achieve stable and controllable energy efficiency optimization, which is of great significance to the research of energy efficiency optimization in the cloud environment.