Research Of Network Traffic Based On Wavelet Analysis

In the field of communications, as a new network traffic model, self-similarity is attracting more and more attention, because it characterizes the essence and trait of real network traffic more accurately than traditional traffic model. Self-similarity has effected the designation, control, analysis and management of network greatly. In traditional study, people look on network traffic as Poisson or Markovian model. But Poisson or Markovian model is not good for characterizing the self-similarity of real network traffic, because they are short-range dependent. Therefore it is necessary to emend many network research results, which are on the base of traditional model. In recently years, wavelet analysis has more and more wide application in network traffic model, and it offers the strong base for using network source efficiently.In this paper, we carry out some significant work as follows:Since Hurst index is the key value of self-similar network traffic, efficient estimation of Hurst index to the given accuracy is the basic step of flow control as well as buffer management in high-speed broadband network (e.g. ATM) with self-similar traffic. We propose a Hurst index estimation method based on DFGN model and Haar wavelet. Simulation results based on DFBM and real traffic data reveal that the method improves accuracy and efficiency compared to the traditional method.Since the application of tradition methods is limited by the self-similarity of network traffic, a long-range dependence reduction method is proposed based on wavelet transform. Compared to K-L Transform, its compute is simpler. Simulation results based on DFBM and real traffic data reveal that the method is efficient.We use a wavelet packet transformation in order to improve the efficiency of data compression. This paper discusses the selection methods of some key factors that affect the performance of data compression based on wavelet transforms, such as threshold, wavelet function, proper level of decomposition and the basis of wavelet packet. We selected the threshold entropy as standard and efficient compression result is got.