Network Traffic Prediction Research Based On Intelligent Algorithm

With the rapid development and more computers linked into the Internet network, the scale of internet is becoming more complicated, in order to realize the goal of resource share, the amount of the network traffic is increasing, the task and stress of network management becomes heavily. And failures and questions appear again and again, it is very important to comprehend the control mechanism and complicated behavioral character. It has important significance through analysis and predictions: It contributes to understand situation, so the network traffic can be accurately predicted and simulated. It's useful for designing and controlling. It can be more effective for optimization, and be better for routing design and load-balanced design. It could determine the network congestion control, which can reduce network congestion due to the loss and delay of information. It can make full use of network resources to improve service quality. It may realize intrusion detection, detect and exclue potential attacks and intrusions. Network traffic prediction is necessary,high quality traffic prediction has significant meanings for large scale network management and design.Purpose of the study is to improve the forecast accuracy and stability of network traffic,the main work is to research and explore new network prediction models.Firstly, in the paper background, significance and research status about network traffic prediction are comprehensively narrated, to make foundation for the following researches.Secondly, the paper analyzes some important characters, including self-similarity, long-range dependence, multifarious characteristics. It's important for mastering traffic prediction essence and improving traffic accuracy.Thirdly,Based on understanding the characteristics about network traffic, several traditional network traffic models are analyzed, which has significant guide meanings for making use of intelligent algorithms for traffic prediction model constructionFourthly, a model based on wavelet packet de-noising and Elman is found.De-noising the traffic series with wavelet packet, then taking the de-noised series as the input of Elman while the predictive series as the output of Elman. By using the N days'de-noised traffic series to forecast the later M days'predictive series. The N days'de-noised series is token as a sliding window and mapped into the later M days'predictive series.Fifthly, a mode based on wavelet analysis and AR-LSSVM is established. Firstly, the series are decomposed, getting a low frequency signal and several high frequency signals, the approximation part and detail parts were reconstructed to the original level, the next sequence of each were predicted using least squares support vector machines and self-regression model, the final, with the various reconstructed sequence, getting the prediction sequence of original sequence, improving the forecast accuracy and stability of network traffic.Sixthly, a mode based on BPNN optimized by QPSO algorithm is established. It considers the flaw of PSO algorithm.QPSO algorithm is applied to optimize the weights and thresholds in BP neural network,and historical records are used to train BP neural network.