Journal of Computer Networks and Communications

Volume 2019, Article ID 2182803, 10 pages

https://doi.org/10.1155/2019/2182803

## A Novel Hybrid Network Traffic Prediction Approach Based on Support Vector Machines

^{1}School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China^{2}Department of Mathematics and Computer Science, Free University of Berlin, Berlin, Germany^{3}School of Information Engineering, Zhengzhou University, Zhengzhou 450000, China

Correspondence should be addressed to Zhihao Shang; nc.ude.uzl@11hzgnahs

Received 28 September 2018; Revised 11 January 2019; Accepted 16 January 2019; Published 12 February 2019

Guest Editor: Saman S. Chaeikar

Copyright © 2019 Wenbo Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### 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.

#### 1. Introduction

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]. Precise network traffic prediction can thoroughly catch the notable attributes of the traffic, and thus it plays a vital role in network traffic analysis and simulation and offers assistance to customers to understand the network dynamics. So, in recent years, to enhance the network traffic prediction precision, researchers in China and abroad have proposed numerous network traffic prediction methods.

In general, 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]. Dickinson [3] has demonstrated that the combined and hybrid models can get better forecasting result than that of individual models. Besides, the topology and geometry of network are always very complex, which often influence the network traffic prediction accuracy. 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. The complexity indicated from the traffic estimations has prompted the development of network traffic prediction, which suggests that a single model cannot yield satisfactory prediction result [4–6]. The main reason behind this is that network traffic displays numerous characteristics, such as trend, cycle time, self-resemblance, and long-area dependence. Network traffic prediction with a single model cannot capture all the characteristics mentioned above. But a combination model can not only capture the linear characteristics but also the nonlinear characteristics of the NTD (NTD). Therefore, the combination model is applied in this paper.

Over the past few decades, scientists over China and abroad have presented a lot of strategies to predict network traffic in diverse areas [7, 8]. Among them, some were more inclined to improve the existing models. For example, the literature [9] prolonged the notion of the broadly stated and used the fractional Brownian traffic model. Qing-Fang et al. [10] used a BIC-based totally neighboring factor choice approach to select the quantity of the nearest neighboring factors for the nearby Support Vector Machines. And, with the intention to obtain quicker convergence in the training of BiLinear Recurrent Neural Network (BLRNN), the literature [11] applied two procedures to the network. Other experts preferred a combination of the existing models. For example, the literature [1] developed a novel combined model to predict the network traffic in the National Taitung University and Shu-Te University. Chen et al. [12] evolved a new bendy neural tree structure which used Gene Programming, and the parameters are optimized through the Particle Swarm Optimization algorithm. The literature [13] presented a novel approach, which integrated wavelet transform, the grey theory, and the chaos theory; the numerical experiment demonstrated that the proposed model can get better prediction results. Recent papers about network traffic prediction methods can be seen in literatures [14, 15].

Despite the fact that the previous mentioned approaches can produce an adequately precise prediction result for various cases, they, in general, have focused on the precision evaluation of the approaches obtained without noting the internal characteristics of the network traffic data (NTD). In truth, NTD are normally influenced by means of risky factors, therefore inflicting noise indicators that can increase the difficulty of forecasting. So in this paper, the EMD is first applied to eliminate the noise signals before applying SVM to predict the network traffic. Besides, the parameters in SVM are optimized by PSO. Therefore, the presented method integrates the EMD, PSO, and SVM, hence its abbreviated name is EPSVM. In order to examine the effectiveness of EPSVM, we contrast it with three other approaches, namely, (1) the original NTD directly processed by SVM (the method is named as BSVM), (2) NTD processed by EMD and then using SVM to model the denoised data (the method is named as ESVM), and (3) the original NTD directly processed by the SVM, whose parameters are optimized by PSO (the method is named as PSVM). Besides, it is noteworthy that the NTD are gathered from the Network Center of Lanzhou University.

The rest of this paper is presented as follows. The theoretical background of EMD, PSO, and SVM models is specified in Section 2. In Section 3, the presented approach is introduced. Section 4 illustrates the experimental results. At last, Section 5 concludes this paper.

#### 2. Theoretical Background of EMD, PSO, and SVM Models

In this subsection, the theories related to the proposed method (EPSVM) are introduced, and they are EMD, PSO, and SVM.

##### 2.1. Empirical Mode Decomposition

Empirical Mode Decomposition (EMD) is a nonlinear signal processing method developed by Huang et al. [15]. It can decompose a signal into a sum of functions and intrinsic mode functions (IMFs). These IMFs must satisfy two conditions: (1) the number of extrema and the number of zero-crossings either are equal or differing at most by one; (2) the mean value of the envelope defined by the local maxima and the local minima is zero at all points. According to [16–18], any signal can then be disintegrated:(1)Identify all the local extrema, and then connect all the local maxima with a cubic spline line as the upper envelope.(2)Repeat the procedure for the local minima to produce the lower envelope. The upper and lower envelopes should cover all the data between them.(3)The mean of the upper and lower envelopes is designated as , and the difference between the signal and is the first component :

Ideally, if satisfies the definition of an IMF, then it is the first IMF.(4)If is not an IMF, is treated as the original signal, and by repeating processes (1), (2), and (3), is acquired. After repeating the sifting process up to times, becomes an IMF, i.e.,

Then, it is designated as

The first IMF component from the original data should contain the finest scale or the shortest period component of the signal.(5)Separating from the original signal , we could get . By repeating the above process several times, the result was . Then, , are the IMFs that were obtained.

##### 2.2. Particle Swarm Optimization

Particle Swarm Optimization (PSO) is one of the recent meta-heuristic technologies proposed by Kennedy and Eberhart [19] in view of the natural flocking and swarming behaviors of birds and insects. Consider an optimization problem of variables. A swarm comprises of particles flying in a dimensional search space. Let denote a particle’s position and denote the particle’s flight velocity over a solution space. Each individual in the swarm is scored utilizing a scoring function that obtains a fitness value representing how good it settles the issue. The best previous position of a particle is . The index of the best particle among all particles in the swarm is . Each particle records its own personal best position () and knows the best positions found by all particles in the swarm (). Then, the best position of particle could be calculated [20]:where is the inertia weight factor, and are two independent randomly distributed variables with the range of [0, 1], and and are two positive constants called acceleration coefficients.

##### 2.3. Support Vector Machines

Support Vector Machine (SVM) [21] is a set of classification and regression techniques, designed to systematically optimize its structure based on the input training data. More details about SVM can be seen in the literatures [22–24].

Given the training data , where denotes the space of the input patterns and is the associated output values of . In -SVR, our goal is to produce a function based on the training data set to approximate the unknown function . By introducing different constraints for violating a “tube” constraint from above and from below, we arrive at the formulation stated in Vapnik’s article [25] for -SVR:where denotes the number of samples, whereas and are the allowed error “above” and “below” the training error subject to -insensitive tube and is the regularization term. The empirically selected constant determines the tradeoff between these two terms.

To preserve the sparse property of the solution, Vapnik used the -insensitive loss function described by

Instead of minimizing the observed training error, -SVR attempts to minimize the generalization error bound so as to achieve generalized performance, and this makes -SVR extremely robust to outliers. Finally, we get the following explicit form by introducing Lagrange multipliers, the Kernel trick, and employing the optimality constraints:

#### 3. The Proposed Method

The proposed method (EPSVM) first uses EMD to eliminate the noise signal, then the data after EMD procedure are put into the SVM, and the parameters of SVM are optimized by PSO. So, in this subsection, the theory of SVM optimized by PSO is introduced in Section 3.1. And then, the specific prediction procedure of EPSVM is presented in Section 3.2.

##### 3.1. SVM Optimized by PSO

The parameters of SVM have an extraordinary effect on the forecasting precision, and it is very important to optimize the two parameters in the forecasting procedure. So, PSO is utilized to optimize the parameters in SVM (which is named as PSVM). The detailed process of PSVM is depicted in Figure 1 which includes the following five steps:(1)Initialization: the quantity of the population is initialized, and the preliminary position and velocity of each particle are randomly allocated.(2)Fitness assessment: for each particle, its fitness is assessed, and the fitness function is calculated as the subsequent:where and stand for the actual and forecast values, respectively.(3)Update and according to the fitness function results.(4)Update the velocity of each particle according to Equation (4) and the position of each particle using Equation (5).(5)Termination: the velocity and position of the particle are updated until the stop conditions are met.