Cloud Database Product Usage Prediction Based on Component Decomposition and Multimodal Fusion

Cloud database technology has been widely used because of its flexible expansion, ease of management, and on-demand charging. Businesses usually select cloud database products based on their specific application scenarios and requirements. Service providers determine the usage of different types of resources, such as computing and storage, to satisfy service requirements. Accurate prediction of cloud product usage is critical for improving resource usage efficiency, reducing operational costs, and ensuring Quality of Service (QoS). However, predicting cloud database product usage is complex. A usage sequence typically comprises multiple interrelated components with complex entanglements. Additionally, the behavioral characteristics of different businesses vary according to cloud products and billing items, which poses a significant challenge for accurate usage prediction. To solve this problem, this study proposes a cloud database product usage prediction model based on component decomposition and multimodal fusion. This model effectively decomposes a time-series with complex entanglement, fuses multimodal demand data, builds a mapping relationship between demand and usage trends, and automatically adjusts the weight parameters of its components to obtain accurate prediction results. In this study, real production data from four major cloud database products from the Ali cloud computing service providers are used to evaluate the prediction effect and the performance is compared with that of five other prediction algorithms. Analyses of evaluation metrics, such as the Mean Absolute Percentage Error (MAPE), reveal that the proposed model improves prediction accuracy to different degrees in the four cloud database products, approximately 18.6%-51.8%. Therefore, this model can be applied to cloud database product usage prediction scenarios and help cloud service providers in improving the accuracy of resource capacity planning.