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Data communication network traffic predicting method based on traffic analysis

A data communication network and traffic prediction technology, applied in data exchange networks, digital transmission systems, electrical components, etc., can solve the problems of slow algorithm convergence speed and poor effect, and achieve good convergence, network performance and service quality improvement. Effect

Active Publication Date: 2017-08-08
ZHONGSHAN POWER SUPPLY BUREAU OF GUANGDONG POWER GRID
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0010] The present invention provides a data communication network traffic forecasting method based on traffic decomposition in order to solve the above technical defects of slow convergence speed of the prior art algorithm or poor effect in long-term network traffic forecasting

Method used

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  • Data communication network traffic predicting method based on traffic analysis
  • Data communication network traffic predicting method based on traffic analysis
  • Data communication network traffic predicting method based on traffic analysis

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0038] Such as figure 1 Shown, the method provided by the invention comprises the following steps:

[0039] S1. Use the R / S sequence analysis method to predict the Hurst parameter value of the flow sequence, and determine whether the flow sequence is in a steady state based on the predicted Hurst parameter value;

[0040] S2. If the flow sequence is judged to be in a stationary state based on the Hurst parameter value, the flow sequence is fractionally differentiated, and the predicted value of the flow sequence is calculated based on the result of the fractional difference; if the flow sequence is determined to be in a non-stationary state based on the Hurst parameter value, Then execute step S3;

[0041] S3. Decompose the flow sequence into two signals by discrete wavelet transform, i.e. based on the selected scaling function φ 0 and wavelet function ψ 0 , and then construct the bandpass wavelet function basis ψ j,k and the low-pass scaling function basis φ j,k :

[00...

Embodiment 2

[0069] This embodiment verifies the performance of the method provided in embodiment 1 through simulation. The data used in Embodiment 1 comes from the traffic files of http:∥newsfeed.ntcu.net / ~news / 2006. The traffic file collects the access traffic of the master node router in different time periods within 5 days, such as figure 2 shown.

[0070] By R / S sequence analysis method, figure 2 The self-similarity index of the medium-time traffic series is 0.9065, indicating that the network traffic has high self-similarity. In addition, it can be seen from the figure that there is a large gap between the maximum flow and the minimum flow of the network, indicating that the network flow is non-stationary and highly bursty.

[0071] The present invention uses wavelet transform to decompose the collected network traffic into two parts, the approximate part and the detailed part, such as Figure 3(a) , 3(b) shown. The approximate part reflects the changing trend of network traf...

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Abstract

The invention provides a method which uses Hurst parameter values to represent self-similarity presented by network traffic, and analyzes statistic characteristics of the network traffic in big time scale in combination with the R / S series analysis method. Since the R / S series analysis method can better describe fractal characters and long-time memory process of the network traffic, and the FARIMA model which is used at later stage can also describe long-range dependence and short-range dependence of the network traffic, according to the invention, the method can conduct long-time prediction on the network traffic in a precise manner, has better astringency, and is very significant in terms of increasing network properties and service quality.

Description

technical field [0001] The invention relates to the field of network traffic management of data communication networks, and more specifically, relates to a network traffic forecasting method using wavelet coefficient perception for traffic decomposition, which can accurately evaluate network performance, ensure stable network operation, and improve network service quality. Background technique [0002] In recent years, with the rapid development of Internet technology, people's requirements for network service quality have become increasingly stringent. However, the explosive growth of network data volume, large-scale networks, poor management of large data traffic and insufficient prevention of malicious data flow attacks have caused Faults such as network congestion and crashes occur frequently, which brings huge challenges to the control and management of network service quality, poses a great threat to network security and performance, and seriously affects user experienc...

Claims

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Application Information

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IPC IPC(8): H04L12/24
CPCH04L41/14
Inventor 包达志张黎李亮亮陈智聪吴浩辉余锦业朱江云刘伟荣刘飞岐
Owner ZHONGSHAN POWER SUPPLY BUREAU OF GUANGDONG POWER GRID
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