Container Load Prediction Algorithm Based on ARIMA Model and BP Neural Network

Docker container technology has brought great changes to the field of cloud computing, which has greatly improved the cloud platform in terms of resource scheduling delay, resource granularity, and resource utilization. Container cloud represented by Docker and Kubernetes has been widely concerned and applied in the cloud computing industry. This paper proposes a container load prediction algorithm based on ARIMA model and BP neural network, which provides a reference for Kubernetes resource scheduling, and verifies the reliability of the algorithm through experiments.