2009/05/31 by Shi‐Min Cai, Shi-Min Cai, Zhong-Qian Fu +4 · 29 citations
Economics, Econometrics and Finance · Mathematics · Physics and Astronomy · #Byte #Combinatorics #Complex Network Analysis Techniques #Complex Systems and Time Series Analysis #Computer network #Computer science #Confidence interval #Detrended fluctuation analysis #Exponential function #Interval (graph theory) #Mathematical analysis #Mathematics #Network packet #Physics #Probability and statistics #Scaling #Statistical physics #Statistics #Theoretical and Computational Physics #physics.data-an #physics.soc-ph
paper · pdf · doi:10.1209/0295-5075/87/68001
published in Europhysics Letters (EPL) 87(6), 68001 (Institute of Physics) · 4 pages, 8 figures
arxiv created 2009/08/23 · openalex publication_date 2009/09/01 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
By studying the statistics of recurrence intervals, τ, between volatilities of Internet traffic rate changes exceeding a certain threshold q , we find that the probability distribution functions, P q (τ), for both byte and packet flows, show scaling property as . The scaling functions for both byte and packet flows obey the same stretching exponential form, f ( x )= A exp (- Bx β ), with β≈0.45. In addition, we detect a strong memory effect that a short (or long) recurrence interval tends to be followed by another short (or long) one. The detrended fluctuation analysis further demonstrates the presence of long-term correlation in recurrence intervals.