Research Of Network Traffic Based On System Dynamics

The analysis and modeling of the network traffic is one of the branches of thefield of traffic technology. Because network congestion detection and control (such asthe efficiency of bandwidth, providing QoS) is connected with the character ofnetwork traffic and because the utilization of network resource is dependent on it also,it has important theoretical and practical meanings on the study of network traffic todesign and optimize the network framework. In the design and optimization of network framework, the model of networktraffic is important. Especially, as a tool to analyze network traffic, time series has abright foreground of application. But traditional time series only deal withshort-dependence process, such as Poisson process, Markov process,AR(Auto-Regressive), MA(Moving Average) , ARMA(Autoregressive MovingAverage) and ARIMA(Autoregressive Integrated Moving Average). With thedevelopment of network measuring, researchers have found that there islong-dependence in the high-speed network, which is also called self-similarity. Themodels above are not in point, so other long-dependence models such as FGN(Fractional Gaussian Noise) and FARIMA (0,d, 0) are used in network traffic. Therecent model of FARIMA (p, d,q) gets over the scarcity of above models, which candeal with long-dependence process and short-dependence process at the same time.While its forecast is based on the probability and its forecast length is confined to thecharacter of network traffic, this restricts it to put into practice. At the present, themethod about the question of time series by system dynamics is developing, which isused in many fields. On the other hand, the self-similarity of network traffic is reallycertificated and self-similarity is connected with chaos closely, so we can use thetheory about chaos to study the model of network traffic. We find that there are fewarticles using chaos theory to study network traffic, and these articles are attentive tothe character of network traffic. All of these suggest us to use chaos theory to studynetwork traffic and to argue about the cause about the chaos of network traffic. The paper analyses the kinetic characteristics of Internet traffic by using somecomplexity theories, such as fractal and chaos theories. We obtain the fractalcharacteristics, the extent of complexity, and the style of movement of the system.Based on the above, we discussed relation between the chaos of Internet traffic andlong-range dependence by filtering the long-range dependence using wavelet analysis.The conclusion as follow: there is fractal structure in the Internet traffic and the chaosof Internet traffic is connected with long-range dependence. With phase spacereconstruction, the paper demonstrates the Internet traffic chaos phenomena lies inInternet traffic, and computes some parameters such as correlative dimension,Lyapunov exponent. Based on this, the paper constructs the BP neutral network modelto forecast the Internet traffic. Comparing with FARIMA (p, d, q) model, the BPneutral network model has the same ability of forecast and has longer space offorecast.