Prediction model of network link traffic in cloud environment

With the continuous development of cloud technology, the services carried by the cloud platform continue to increase, which brings higher requirements to the network link resource planning and operation management. Establishing a link traffic prediction model for the actual needs of the network enables network operators to The rapid decision-making of resource scheduling is more effective with the assistance of advanced control and forwarding technologies such as SDN. Existing prediction methods do not consider the deep fine-grained features of current network traffic that are more diverse and more complex. By improving the LSTM model, this paper is oriented towards scientific research. To meet the management requirements of private networks, a new link traffic prediction model is proposed. The AE-stacked hybrid LSTM model consists of autoencoder AE, bidirectional LSTM model, unidirectional LSTM model and fully connected layer. The ability to extract traffic features can better mine the dependencies between data features at different times. The experimental results show that the prediction results of this model are in line with the real trend of traffic changes, and the residual between the predicted value and the observed value is small. It can better fit the existing traffic of the scientific research network.