Research on Cloud-Based Safe Computing Platform Resource Prediction

In order to ensure the safe operation of cloud-based safe computing platform in train control system, it is crucial to accurately predict cloud platform resources. Due to the different number of trains at different times and sections, the signal software load in cloud based security computing platforms also varies. Resource data often accompanies high randomness and non-stationary characteristics, increasing the difficulty of prediction. A hybrid prediction model based on Sparse Search Algorithm (SSA), Variational Mode Decomposition (VMD), SE module, and Time Convolutional Network (TCN) is proposed to address the low prediction accuracy of traditional prediction methods and the lack of research on cloud platform in train control system. First, the original data is decomposed using the VMD optimized by SSA, and then each component is predicted using the SE-TCN model. Finally, integrate the prediction results of each component to obtain the final cloud resource usage prediction result. The experimental results show that, compared with other models, the Mean absolute error (MAE) of the proposed model on the memory data are reduced by 27%, and the CPU data are reduced by 24%, with high prediction accuracy.