Server load prediction method based on deep learning

Abstract

The present invention discloses a server load prediction method based on deep learning, collecting the trend change of server load long series, and utilizing server load periodic information to establish a deep neural network prediction model to optimize peak load prediction. The present invention provides a method for improving the accuracy of neural network prediction by combining periodic information, long-term trend information, and short-term time series information, and demonstrates superiority over traditional methods in the peak load section. The method of the present invention can effectively improve prediction accuracy, provide more accurate scheduling and evacuation decision-making basis for cloud service providers, thereby reducing the redundant equipment required to ensure high reliability, reducing the operating costs of cloud service providers, and reducing the rental expenses of cloud service tenants.