Traffic Analysis And Prediction Of IP Network

Along with the development of the Internet, the attendant is constantly expanding its scale, the increasing complexity of the bandwidth and doubled. But in it is far from enough in-depth knowledge, network measurement in such circumstances came into being, Based on the network traffic measurement, modeling, forecasting and analysis, thus achieving the network of observation and assessment.In this paper, network measurement and network management related to the basic concepts and theories. Second, the detailed description of network traffic flow measurement technology and data collection and model analysis. The flow of data in data acquisition, the use of a data packet capture technology, based on JPCAP Acquisition Ethernet data sharing. Due to the development of high-speed network technology for the entire data packet flow measurement spending more, this paper introduces the method of sample collection to be addressed. Finally this paper flow to the day-to-day users example, the subjects described in the flow forecasts and analysis of the entire process, including the flow of data acquisition, flow forecast model, flow forecast and the actual value of the comparative analysis.To achieve this topic in the IP network environment on a day-to-day flow of the user's collection, and the collected data packet sampling, based on sample data selected characteristics AR (P) a mathematical model of flow forecast, and forecast flow data and the actual data were analyzed. In the acquisition area, the paper used JPCAP listening mode data collection, forecasting, as this paper is a smooth series model, this paper AR, MA, ARMA model. Based on the sample images of (φ|^)_kk and found several features of this sequence with Censored, the order of the model for 2, so this sequence suitable model AR (2). In the analysis, flow through predictive value and the actual value of the comparative analysis, found that the peak flow of two graphics number of points, graphics and graphics broadly similar to the basic level of similarity.