A hierarchical clustering scheme approach to assessment of IP-network traffic using detrended fluctuation analysis

作者：Takuma Takehisa; Masugi Masao^*

来源：Communications in Nonlinear Science & Numerical Simulation, 2009, 14(3): 697-707.

DOI：10.1016/j.cnsns.2007.11.003

• Network traffic
• Self-similarity
• Long-range dependence
• Detrended fluctuation analysis
• Hierarchical clustering

This paper presents an approach to the assessment of I P-network traffic in terms of the time variation of self-similarity. To get a comprehensive view in analyzing the degree of long-range dependence (LRD) of IP-network traffic, we use a hierarchical clustering scheme, which provides a way to classify high-dimensional data with a tree-like structure. Also, ill the LRD-based analysis, we employ detrended fluctuation analysis (DFA), which is applicable to the analysis of long-range power-law correlations or LRD in non-stationary time-series signals. Based on sequential measurements of IP-network traffic at two locations, this paper derives corresponding values for the LRD-related parameter alpha that reflects the degree of LRD of measured data. In performing the hierarchical clustering scheme, we use three parameters: the alpha value, average throughput, and the proportion of network traffic that exceeds 80% of network bandwidth for each measured data set. We visually confirm that the traffic data can be classified in accordance with the network traffic properties, resulting in that the combined depiction of the LRD and other factors can give us all effective assessment of network conditions at different times.

• 出版日期2009-3