Virtual machine selection strategy based on ARIMA predictions of CPU resource utilization

The application of virtualization technology in cloud computing and data center management has become a key trend. In order to optimize resource utilization and performance, the selection and deployment strategy of virtual machines are crucial. This paper proposes a virtual machine CPU resource utilization prediction model based on ARIMA (AutoRegressive Integrated Moving Average) and applies it to the optimization of virtual machine selection strategy. We use historical virtual machine performance data to train the ARIMA model to forecast future CPU resource utilization, supporting more intelligent and adaptive decisions for virtual machine deployment. Through extensive experiments and performance evaluations, we have demonstrated the effectiveness of this strategy under different workloads and environmental conditions.