Delayed Correlations in Inter-Domain Network Traffic
Viktoria Rojkova, Mehmed Kantardzic

TL;DR
This paper investigates the delayed correlation structures in inter-domain network traffic using eigenvalue spectra and eigenvector analysis, revealing long-range dependence and implications for congestion control.
Contribution
It introduces a novel analysis of delayed correlation matrices in network traffic, highlighting long-range dependence and the impact of periodic injections on eigenvalues and IPR.
Findings
Delayed correlations persist up to 100τ (300 sec)
Eigenvalue λmax oscillates with periods of 3τ and 6τ
Injecting random traffic breaks periodicity of λmax
Abstract
To observe the evolution of network traffic correlations we analyze the eigenvalue spectra and eigenvectors statistics of delayed correlation matrices of network traffic counts time series. Delayed correlation matrix D is composed of the correlations between one variable in the multivariable time series and another at a time delay \tau . Inverse participation ratio (IPR) of eigenvectors of D deviates substantially from the IPR of eigenvectors of the equal time correlation matrix C. We relate this finding to the localization and discuss its importance for network congestion control. The time-lagged correlation pattern between network time series is preserved over a long time, up to 100\tau, where \tau=300 sec. The largest eigenvalue \lambda_{max} of D and the corresponding IPR oscillate with two characteristic periods of 3\tau and 6\tau . The existence of delayed correlations between…
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Taxonomy
TopicsNonlinear Dynamics and Pattern Formation · Theoretical and Computational Physics · Complex Network Analysis Techniques
