Studying On Chaos Characteristics And Prediction Of Network Traffic

As the rapid development and application of Internet, the scale of internet is becoming larger and larger, the application of the internet is becoming more and more complicated. Due to network is a very complicated non-line system, in order to realize reliable data transfer and reasonable internet resource distribution, it is very important to comprehend the control mechanism and complicated behavioral character of network.Distributed applications use predictions of network traffic to sustain their performance by adapting their behaviors. It's important that we must firstly know the character of network traffic. It has been recognized that the network traffic of non-linear part has chaos characteristics. However, existent network traffic prediction study only utilize either linear or non-linear methods to solve the problem and neglect chaos characteristics.The paper bases on chaos characteristics introduce the way of phase space reconstrunction and parameter counting. And experimentalize to prove the theory.Bases on warelet transform and nonlinearity dynamics method to study chaos characteristics of network traffic flow, and improve phase space reconstrunction. Chaotic attractor was projected in the space of wavelet filter vector, and having fully utilized with warelet denoising. And prove the new method of network traffic prediction is advantage. Introduce the method of chastic time series predictior based on warelet neural network traffic flow prediction.And wavelet transform provide a affective new method of network traffic prediction.