Abstract: Cloud platform resource prediction is of great significance for resource management and energy saving.Cloud VM technology is a virtualization method implemented by the cloud to make full use of physical resources,but effective cloud VM load prediction is still challenging,because the cloud VM load has periodic and aperiodic change patterns and sudden load peaks,and the cloud VM load is affected by the random submission of jobs by users.In order to accurately analyze the change mode of VM load and improve the performance of VM CPU load prediction,a cloud VM load prediction method based on decomposition-prediction is proposed.Through EMD and PCA of cloud VM load mode decomposition,the characteristic fluctuation sequences of different time scales are obtained.The convolution layer of the prediction model can fully extract the decomposed features,and learn the forward and backward dependencies of the sequence through the bidirectional gated cyclic neural network,which improves the ability of the prediction model to learn the load change mode of the VM.Finally,single-step and multi-step prediction experiments are performed on the 2019 VM data sets generated by Microsoft Azure in the real cloud environment,which verifies the effectiveness of the prediction method.

Key words: Cloud VM, Decomposition mode, Intrinsic mode function, Load prediction, Neural network model