Abstract
Network traffic prediction performs a main function in characterizing network community performance. An approach which could appropriately seize the salient characteristics of the network visitors could be very useful for network analysis and simulation. Network traffic prediction methods could be divided into two classes: one is the single models and the opposite is the hybrid fashions. The hybrid models integrate the merits of several single models and consequently can enhance the network traffic prediction accuracy. In this paper, a new hybrid network traffic prediction method (EPSVM) primarily based on Empirical Mode Decomposition (EMD), Particle Swarm Optimization (PSO), and Support Vector Machines (SVM) is presented. The EPSVM first utilizes EMD to eliminate the impact of noise signals. Then, SVM is applied to model training and fitting, and the parameters of SVM are optimized by PSO. The effectiveness of the presented method is examined by evaluating it with different methods, including basic SVM (BSVM), Empirical Mode Decomposition processed by SVM (ESVM), and SVM optimized by Particle Swarm Optimization (PSVM). Case studies have demonstrated that EPSVM performed better than the other three network traffic prediction models.
Highlights
It is generally known that network traffic prediction can provide a variety of practical information for Internet organizations, for example, about travelling, rental company, and smart search
Network traffic prediction methods can be divided into two categories: one is the single models and the other is the combination, i.e., hybrid model which integrates the merits of several single models [2]
Demonstration of network traffic complexity shows up in numerous circumstances, for instance, the long-area connections and self-resemblance were found in a statistical analysis of traffic estimations. e complexity indicated from the traffic estimations has prompted the development of network traffic prediction, Journal of Computer Networks and Communications which suggests that a single model cannot yield satisfactory prediction result [4,5,6]. e main reason behind this is that network traffic displays numerous characteristics, such as trend, cycle time, self-resemblance, and long-area dependence
Summary
It is generally known that network traffic prediction can provide a variety of practical information for Internet organizations, for example, about travelling, rental company, and smart search. Network traffic prediction is a procedure whereby a webmaster catches the network traffic and inspects it closely to discover what is going to happen in the follow-up and coming period on the network. It can assist each webmaster by establishing reasonable network planning and controlling the network traffic congestion effectively [1]. Demonstration of network traffic complexity shows up in numerous circumstances, for instance, the long-area connections and self-resemblance were found in a statistical analysis of traffic estimations. E complexity indicated from the traffic estimations has prompted the development of network traffic prediction, Journal of Computer Networks and Communications which suggests that a single model cannot yield satisfactory prediction result [4,5,6]. A combination model can capture the linear characteristics and the nonlinear characteristics of the NTD (NTD). erefore, the combination model is applied in this paper
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