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A Bayes Theory-Based Modeling Algorithm to End-to-end Network Traffic

Hong-hao Zhao, Fan-bo Meng, Si-wen Zhao, Si-hang Zhao, Yi Lu, T. Gong, T. Yang, J. Xu

2016
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ITM Web of Conferences
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Recently, network traffic has exponentially increasing due to all kind of applications, such as mobile Internet, smart cities, smart transportations, Internet of things, and so on. the end-to-end network traffic becomes more important for traffic engineering. Usually end-to-end traffic estimation is highly difficult. This paper proposes a Bayes theory-based method to model the end-to-end network traffic. Firstly, the end-to-end network traffic is described as a independent identically
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... entically distributed normal process. Then the Bases theory is used to characterize the end-to-end network traffic. By calculating the parameters, the model is determined correctly. Simulation results show that our approach is feasible and effective. Introduction With the quick development of network technology and new applications, network traffic exhibits new features. This leads to a new challenging for network engineering [1] [2] . it is important to accurately characterize and model network traffic for improving network performance. The features of network traffic, such as self-similarity nature, auto-correlations, heavy-tailed distribution, have an important impact on network optimization and routing [3] [4] . The end-to-end network traffic represents the network-wide behaviors from a global point of view. Hence, modeling the end-to-end network traffic has received an extensive attention from researchers, operators, and developer all around the world [5] . The end-to-end traffic behaviors embody the pathlevel feature in the network. It can be used to describe network status and nature, such as path loads, throughput, network utilization, and so on. The statistical methods are employed to denote the model of network traffic from the source node to the destination node [1,3]. The gravity model [4] , generic evolvement [6-7], mix method [2], and compressive sensing are utilized to capture the properties of the end-to-end network traffic. These method can attain the better prediction and estimation of the end-toend traffic by performing a modeling process. However, these methods need the additional information from link loads or a prior information about the end-to-end network traffic. This adds the computational complexity and overhead for attaining the model parameters. The timefrequency domain analysis can be used to capture the multi-scale features and dynamic nature [1, 8] . The neural network is employed to model network traffic [7, 9] . These approaches can build the model to denote the endto-end network traffic, while it is very difficult to exactly capture and seize their features and to build the accurate and appropriate network traffic model for traffic engineering. This paper proposes a end-to-end network traffic modeling method to accurately characterize their features. Generally, it is significantly impossible to directly build the model about them due to their complex properties. Different form previous methods, we use the Bayes theory to establish the model about the end-to-end network traffic. Firstly, we denote the end-to-end network traffic as a independent identically distributed normal process. In the random process, there are several parameters to be estimated accurately. This is very difficult for the limited traffic information. Secondly, to this end, we use the Bases theory to characterize the endto-end network traffic. By calculating the parameters with statistical methods, the model is determined correctly. In such a case, the model about the end-to-end network traffic is correctly built. Thirdly, we propose a new algorithm to build the model. Simulation results show that our approach is feasible and effective. The rest of this paper is organized as follows. Our method is derived in Section 2. Section 3 presents the simulation results and analysis. We then conclude our work in Section 4. Problem Statement The modeling problem of the end-to-end traffic is difficult. In the network, there exists a lot of many end-

doi:10.1051/itmconf/20160709024
fatcat:cpgce3equ5bujltd6rhxnjlxti