Private cloud resource prediction based on combinatorial optimization algorithm

Abstract

In private cloud environments, resource usage is determined based on experience, which often leads to excessive resource allocation and low resource utilization. This paper proposes a private cloud resource consumption prediction algorithm based on combinatorial optimization algorithm. Based on the virtual machine performance data collected from the private cloud platform, the key features are selected by recursive feature elimination method (SVM-RFE), and then the model is trained by eXtrem Gradient Boosting (XGBoost) algorithm for model training to predict the resource usage. Compared with Random Forest, LSTM and other algorithms, the proposed algorithm has higher prediction accuracy and smaller prediction error. This paper adopts a data-driven approach to achieve intelligent prediction of resource usage in private cloud environments, which improves resource utilization and provides decision support for optimal allocation of resources.