Internet Traffic Forecast

With the fast development of Internet, people's living style and methods of traditional information exchange have been affectted seriously. Various application requirements and Internet scale-up lead to much more difficulties on the network management. Network congestion, security and malfunction etc. have influence on the normal development of the information society. Internet traffic measure and forecast have huge significance on the management, layout and design of large-sized network. Nowadays, Netflow method provided by Cisco is used comprehensively and likely to be the factual standard on measure. Support Vector Machine, a newly machine learning algorithm, having some unique merits such as rapidness of solution and strong generalization performance, shows good performance on nonlinear regression.The main research contents and feature of this paper are as follows:(1) This paper introduces the traffic measure methods in common used. SNMP measure collects traffic through the variants in the MIB provided by SNMP network agents. Packet sniffing measure uses NIC (Network Interface Card) to get the traffic at the Link layer. And netflow measure gets traffic by the data exchange mechanisim.(2) The paper introduces some forecast methods in common used. Such as Min-average error method, Artificial Nerual Network method, Self-regression glide average model method and ARMA model method etc. The paper mainly studys the SVM (Support Vector Machine) method based on the statistics theory.(3) The paper introduces the structure of NS2 software pack, NS2 simulation principle and process. Generate traffic data by NS2 simple network topology. Make difference operation on those data and orgnize training data and checkout data. Build SVM forecasting model, get model parameters by traning data, and verify the forecast accuracy by the checkout data. The result on the precision of forecast is good and feasible.(4) Based on the feasible of simulation, using Netflow measure to get traffic in the real circumstance, and forecasting. Through making Netflow configuration on router of Network Mangement Center, collects Netflow packets and computes traffic by related Netflow tools on an appointed PC. Forecasting on that traffic, the result shows SVM regression method having good effect even if the traffic is high nonlinear and huge oscillation.