Abstract: With the rapid development of cloud computing technology,more and more users choose to use cloud services,and the problem of mismatch between load requests and resource supply becomes increasingly prominent.As a result,user requests cannot be timely responded,which greatly affects the cloud service quality.Real-time prediction of load requests will help the timely supply of resources.To solve the problem of low performance of load prediction methods in the cloud computing environment,a cloud computing load prediction method based on hybrid model of complete ensemble empirical mode decomposition with adaptive noise and convolutional long short-term memory(CEEMDAN-ConvLSTM) is proposed.To begin with,the data sequence is decomposed into several sub-sequences which are easy to analyze and model.Then the convolutional long short-term memory(ConvLSTM) prediction model is used to predict the series of sub-sequences.The research idea based on multi-process parallel computation is adopted to realize multi-sequence parallel prediction and Bayesian optimization parameter tuning.Finally,the prediction values are integrated and superimposed to obtain the prediction output of the whole model,to achieve the goal of high-precision prediction of the original complex sequence data.The CEEMDAN-ConvLSTM hybrid model is verified by using the Google cluster workload data set.Experiment results show that the CEEMDAN-ConvLSTM hybrid model had a good prediction effect.Compared with the autoregressive differential moving average model(ARIMA),long short-term memory network(LSTM) and the convolutional long short-term memory(ConvLSTM),the Root Mean Square Error(RMSE) increases by 30.9%,30.1% and 22.5%,respectively.

Key words: Cloud computing, Load prediction, Convolutional long short-term memory(ConvLSTM), Modal decomposition technique, Bayesian optimization