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Study on the Network Traffic Prediction Model Based on the Wavelet Neural Network

Author: YinMing
Tutor: YuYinHui
School: Jilin University
Course: Electronics and Communication Engineering
Keywords: Wavelet Analysis Neural Networks Flow projections
CLC: TP393.06
Type: Master's thesis
Year: 2011
Downloads: 135
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Abstract


With the rapid development of IT technology, network, and how to achieve network QoS control, better network management and maintenance, it is an urgent problem to solve. Network traffic prediction plays an important role in achieving the network QoS control and intrusion detection. The practical significance of the network traffic forecast by more and more people's attention. To accurately predict the network traffic, it is necessary to establish an appropriate model, and accurate understanding of the characteristics of the network traffic is to establish the basis of a correct model. However, through analysis and experimental studies show that the reality of the network traffic is very complicated. If a model can not accurately capture the statistical characteristics of the actual network traffic will result in poor network performance, there will be too high or too low, estimated on network performance. Has been the study of the characteristics of the network traffic has been using the Erlang model, Poisson process, packet arrival process is no memory, the packet arrival intervals are exponentially distributed. W.Leland et al in 1994 reveal the scale of the network traffic characteristics by analyzing the traffic data measured in the 10M Ethernet LAN, network traffic study has entered a new historical stage. Network traffic with a variety of features: self-similarity, long-range dependence, and heavy-tailed distribution character of chaos, fractal and scaling properties (including time and space). In this case, a single model is not suitable for the analysis and forecasting of this complex flow, wavelet analysis to its multi-resolution and response to the sudden advantage, is widely used. Another neural network for its excellent nonlinear fitting characteristics are also more and more people pay attention. With the above advantages combine the two combination model has a broad application space. Based on the characteristics of wavelet and neural network, a model based on wavelet and neural network combined. The experiment used Changchun, a the operators switch port flow, the data were collected in 100 days, 24 hours a day, the whole point of time port traffic 90 days ago, as the data of the neural network training, 10 days after making projections by wavelet transform raw traffic data series can be decomposed into the details of the approximate part, then single reconstructed approximation part and details part respectively; using BP neural network and RBF neural networks to predict each reconstructed sequence, respectively; Finally, these predicted values ??that predict the results of the original traffic data. Real traffic and forecast traffic forecast for later comparison. And obtained variance than the error. We use this method to predict the experiment achieved the desired results, experiments show that this model can accurately predict the network traffic to a certain extent.

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