Research on Cloud Computing load forecasting based on LSTM-ARIMA combined model

With the continuous development of cloud computing technology, the change of cloud computing resource load presents more and more complex characteristics, and efficient load prediction has become a key technology to solve the imbalance of cloud computing resource utilization. Aiming at the problem of low prediction performance of the current load prediction model, a combined prediction model LSAR based on long short-term memory network LSTM and autoregressive moving average model ARIMA is proposed by comprehensively considering the factors of prediction accuracy and prediction time. Compared with the traditional load prediction models ARIMA and LSTM, the open data set was used for the experiment. The experimental results show that the prediction accuracy of the cloud computing resource combination prediction model is significantly higher than that of other prediction models, and the real-time prediction error of resource load in the cloud environment is significantly reduced.