Multi-fractal network flow reconstruction method

A network traffic and multi-fractal technology, applied in the field of multi-fractal network traffic reconstruction, can solve problems that do not conform to the actual traffic distribution characteristics, and achieve the effect of accurate reconstruction

Active Publication Date: 2013-09-18
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0005] The purpose of the present invention is to overcome the existing multi-fractal wavelet network traffic model that the selection of coefficient calculation distribution does not conform to the actual traffic distribution characteristics, and provide a multi-fractal network traffic reconstruction method with reasonable selection coefficients to achieve more network traffic. Granular, Accurate Refactoring

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[0038] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention. It should be noted that in the following description, when detailed descriptions of known functions and designs may dilute the main content of the present invention, these descriptions will be omitted here.

[0039] The network traffic reconfiguration method of the present invention is realized based on wavelet transform. Firstly, the wavelet multi-scale analysis is introduced as follows:

[0040] Wavelet transform has the characteristics of multi-scale analysis, and it is a process of gradually decomposing signals into different frequency signals. The network traffic sequence is decomposed into the sum of multiple simple signals of different frequencies after wavelet transform. These simple signals are obtained by stretching and translating a wavelet function. This wavelet func...

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Abstract

The invention discloses a multi-fractal network flow reconstruction method. According to the method, a roughest scale coefficient and a random multiplication factor are generated and determined by adopting pareto distribution of a heavy tail distribution characteristic, so that the non-negativity of scale coefficients and wavelet coefficients is ensured, the reconstructed flow is accordant with the heavy tail distribution characteristic of practical flow, and the network flow can be reconstructed more finely and accurately. Network flow signals reconstructed with the method disclosed by the invention is applied to network analysis and management activities such as network planning, load balance, blockage control, buffer design and the like.

Description

technical field [0001] The invention belongs to the technical field of network flow reconfiguration, and more specifically relates to a multi-fractal network flow reconfiguration method. Background technique [0002] With the rapid development of network services and the rapid increase of network bandwidth, network traffic has become more and more complex, showing many characteristics such as non-Gaussian, non-stationary, multi-fractal and heavy-tailed distributions. Researchers have found that actual network traffic sequences have self-similar characteristics. In order to better manage and maintain the network, effective measures are taken to extract network characteristic parameters, analyze network performance, optimize network configuration, and discover potential threats. The network traffic model is the basis for network performance evaluation, understanding and analysis of network behavior and its changing rules. Various network traffic models based on self-similar lo...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/24
Inventor 徐杰代云亮孙健隆克平
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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