Recurrence Plot-Based Approach to the Analysis of IP-Network Traffic in Terms of Assessing Nonstationary Transitions Over Time

This paper presents a recurrence plot scheme approach to the analysis of nonstationary transition patterns of IP-network traffic. In performing a quantitative assessment of dynamical transition patterns of IP-network traffic, we used the values of determinism (DET) defined by the recurrence quantification analysis (RQA). Also, in evaluating fractal-related properties of IP-network traffic, we employed the detrended fluctuation analysis (DFA), which is applicable to the analysis of long-range dependence (LRD) in nonstationary time-series signals. Furthermore, to obtain a comprehensive view of network traffic conditions, we used a self-organizing map, which provides a way to map high-dimensional data onto a low-dimensional domain. When applying this method to traffic analysis, we performed two kinds of traffic measurement in Tokyo, Japan, and derived values of DET and the LRD-based scaling parameter alpha of IP-network traffic. Then, we found that the characteristic with respect to DET and self-similarity seen in the measured traffic fluctuated over time, with different time variation patterns for two measurements. In training the self-organizing map, we used three parameters: average throughput, variation ratio of DET, and alpha value. As a result, we visually confirmed that the traffic data could be projected onto the map in accordance with traffic properties, resulting in a combined depiction of the effects of the DET and network utilization rates on the time-variations of LRD