Cloud Computing Resource Load Prediction Based on ISSA-CNN-LSTM-SES Model

In recent years, many researchers have focused on cloud computing resource load issues and proposed various load prediction models. This paper employs an improved Sparrow Search Algorithm (ISSA) to optimize the hyperparameters of the CNN-LSTM combined model and the Single Exponential Smoothing (SES) model. The predicted load values from both models are combined using weight coefficients. As a result, the ISSA-CNN-LSTM-SES load forecasting model is constructed. Experimental results show that ISSA performs well in optimizing model parameters, and promotes the accuracy of load prediction.