NETWORK TRAFFIC PREDICTION MODEL BASED ON OPTIMISING SVM WITH IMPROVED CUCKOO SEARCH ALGORITHM
Network traffic modelling and prediction is the base of network management and safety early warning.In order to improve the accuracy of network traffic prediction,we present a nctwork traffic prediction model which is based on optimising the support vector machine with the improved cuckoo search algorithm (MCS-SVM).First,we reconstruct one-dimension time series of the network traffic to a multidimensional time series,then we regard the parameters of support vector machine as the bird nest,and find out optimal parameters through simulating the parasitism mechanism of cuckoo population,finally we build the network traffic prediction model according to the optimal parameters,and test the performance of MCS-SVM by simulation experiments.The simulation results show that compared with reference models,the proposed model improves the accuracy of network traffic prediction and can more precisely describe the complex variation trend of network traffic,it provides a new research tool for the prediction of network traffic with chaotic property.
Network traffic predictionSupport vector machineCuckoo search algorithmChaotic theory
国家科技期刊平台
NSTL
万方数据
维普
