A Novel IP Traffic Prediction Method of Chaos Theory with Support Vector Regression

IP traffic prediction plays an important role in network-layout, traffic-management, as well as the emphasis of traffic-project, congestion-control and network management. Poor prediction performance would be acquired generally as a result of intense nonlinearity of networks traffic. To tackle it, a modeling method for exact representing IP trafficpsilas movement tendency and a regression algorithm with powerful nonlinear approaching ability should be employed. Consequently, chaos theory and support vector machine (SVM) win the bid. Then, an improved algorithm based-on local SVM method for small scale data-set is proposed. Experimental results demonstrate the validity of improvement by a real-life paradigm that successful forecasting with continuously daily IP traffic during a few days gathered from campus network.