Data Center Energy Management Based on Cloud Computing and Artificial Intelligence

Cloud computing (CC hereafter) is a relatively new technology, which has the characteristics of high resource utilization, flexible management and good scalability. However, because a large number of computing and storage resources are concentrated in the cloud, it becomes more difficult to effectively manage energy. Hence, this paper proposes a nonlinear energy consumption model based on artificial intelligence (Al) and CC. The main components of energy consumption, such as central processing unit (CPU), memory and hard disk, were calculated, and statistics and regression analysis were carried out on the utilization rate of each component. Subsequently, the corresponding energy consumption prediction model was obtained. In the energy consumption model, this paper fully considered the influence of CPU on the energy consumption of other components, and designed the influencing factors between components so as to ensure the accuracy of the model. In the energy consumption model, the impact of CPU on the energy consumption of other components was taken into account, and the factors impacting the various components were designed to ensure the accuracy of the model. From the analysis of the nonlinear model, it is evident that the highest and lowest predicted values of the linear segmented model were 147 and 72, respectively. In the linear single-line model, the highest and lowest predicted values were 163 and 80, respectively. The highest and lowest predicted values under the nonlinear single-line model were 153 and 85 respectively. The highest and lowest predicted values under the nonlinear segmented model were 174 and 97, respectively. Therefore, it is very necessary to study the energy management of data center (DC hereafter) by using an Al algorithm. © 2024 CRL Publishing Ltd.