Research On Network Traffic Modeling And Predicting Based On Time Correlation

As an effective approach to managing, maintaining and securing networks, themeasured data obtained from network measurement can provide the necessaryreference to the upgrade and alteration of the network in terms of monitoring thenetwork environment and network service status. In network measurement, since thenetwork traffic incorporates all information about network operating, networktraffic is the most fundamental and critical data. Therefore, it is very important toanalysis and research network traffic data. With the development of Internet ofThings (IoT) and ubiquitous networks, the network traffic in backbone nodes andlocal area network (LAN) nodes of the next generation Internet is explosivelygrowing and these phenomenons indicate that we are towards a Big Data era. In thiscontext, characteristics of network data are dramatically changed by diversities ofnetwork services, and traditional traffic model does not work for the analysis andpredication of the next generation Internet. Therefore, it is imperative to investigatethe establish-ment of network traffic.Network models can be used to generate network data with differentcharacteristics, and used to test the functionalities and qualities of network devices.These can address the matching problem between network devices and servicetraffic, which has a prominent effect on developing network devices for nextgeneration networks. Network traffic model can also be used to predict future traffic.As an effective indicator, traffic predication can indicate the trend of future networktraffic, and people can adjust the associated network resources to guarantee thequality of service (QoS) according to this trend. By predicting the traffic, someabnormal behaviors can be detected in advance, and some strategies can be taken toreduce the cost as low as possible. This dissertation is devoted to identifying thecharacteristics of network traffic and to predict the traffic trend. The main contentsare as follows.(1) Analysis on characteristics of network traffic. For traffic modelling, trafficcharacteristics are fundamentals. The difference between infinite series of second-order moment function of Possion process and that of self-similar process is firstlyinvestigated. Then the change trend of aggregate traffic of Possion process and self-similar process is provided in different scales. It is shown that the scale feature inself-similar process is determined by its long range dependence, indicating that theanalysis on long range dependence is the key to network traffic modelling.Advantages and disadvantages of several traffic models are compared, in which ON/OFF model and ARMA based models are highlighted. These can providetheoretic fundamentals to C-ON/OFF and EMD-ARMA models discussed in laterparts.(2) Establishment of C-ON/OFF model. By observing the complexity andphysical interpretation issues in long range dependence of traditional models, amodified ON/OFF model is established after research of ON/OFF model’scharacteristics with heavy-tailed distribution and Internet users’ behaviorhomogeneity. By analyzing Hurst parameters and attenuations of autocovariancefunctions, the correlation between ON/OFF sources and long range dependencenetwork traffic is identified. Based on these results, a simple and low-complexitywhile full of physical meaning model, namely C-ON/OFF model, is proposed. Byquantifying the modeling parameters in C-ON/OFF models, some naturerelationship between model parameters and traffic long range dependence is found,further revealing the relationship between long range dependence nature of networktraffic and network convergence, and providing technical reference for futurenetwork traffic modeling.(3) Establishment of EMD-ARMA model. Through the analysis of empiricalmode decomposition (EMD), both theoretical analysis and simulation experimentsprove that the intrinsic mode function of the long range dependence data obtainedafter EMD is short range dependence flow data. The short range dependence modelhas the advantage of lower complexity than long range dependence. Based onEMD’s operation of the removing long range dependence, as well as the advantagesof low complexity of the ARMA model, EMD-ARMA model is proposed.Parameters estimation methods of EMD-ARMA model are compared and discussedin detail. Based on two types of empirical data from the Internet, characteristics ofEMD-ARMA are verified in terms of normalized autovariance, showing that theproposed EMD-ARMA model not only reduces the long range dependence, but alsoreduces the computational complexity. This will provide a solid fundamental fornetwork traffic predication.(4) Traffic predication of EMD-ARMA model. It is proved that, a generalizedoptimal predictive value of the time series of network traffic in the minimum meansquare error exists and is unique. The nature of the optimal linear conditions meansquare prediction value is also studied. The structure of the EMD-ARMA model ofone step and multisteps ahead prediction system is given. Facing system errors inone step ahead prediction, after in-depth research, a scheme to enhance the accuracyof the method is obtained. This further simplifies model, reduces the computationalcomplexity of the model. Relationship between prediction errors and prediction steps in multisteps ahead prediction system is mathematically found, and is verifiedthrough simulation experiment, and according to the updated data. Based on theamount of information in a multisteps ahead prediction system, the correctionmethod for multisteps ahead prediction is given. It is shown that EMD-ARMAmodel is more suitable for short-term prediction of network traffic, networkresource allocation, exception monitoring and has important applications.