Parallel Traffic Real-time Prediction Model for Cloud Network Convergence
Cloud network convergence is the combination of cloud and network.Cloud intelligence technology provides sup-port for dynamic regulation of network resources.In order to control the network resources in time and on demand,it is necessary to predict the network traffic information in advance.Therefore,the prediction of network traffic has become the premise and guarantee of dynamic management of network resources.A parallel traffic real-time prediction model is proposed.By running GARCH and LSTM models in parallel,the real-time prediction of future network traffic can be achieved.The model combines the advantages of linear model and nonlinear model.It can not only predict the periodic and regular flow value,but also predict the random and sud-den large flow value.Through the comparative experiment of parallel traffic prediction model,single GARCH model and single LSTM model,the results show that the root mean square error(RMSE)of parallel traffic prediction model is 4.403%lower than that of single LSTM model and 5.833%lower than that of single GARCH model.In addition,the average absolute error(MAE)and the average absolute percentage error(MAPE)are reduced.
cloud network convergencetraffic predictionLSTMGARCHparallel model
万方数据
维普
