Abstract
Modeling of network traffic is a fundamental building block of computer science. Measurements of network traffic demonstrate that self-similarity is one of the basic properties of the network traffic possess at large time-scale. This paper investigates the change of non-stationary self-similarity of network traffic over time, and proposes a method of combining the discrete wavelet transform (DWT) and Schwarz information criterion (SIC) to detect change points of self-similarity in network traffic. The traffic is segmented into pieces around changing points with homogenous characteristics for the Hurst parameter, named local Hurst parameter, and then each piece of network traffic is modeled using fractional Gaussian noise (FGN) model with the local Hurst parameter. The presented experimental performance on data set from the Internet Traffic Archive (ITA) demonstrates that the method is more accurate in describing the non-stationary self-similarity of network traffic.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Leland W E, Taqqu M S, Willinger W, et al. On the self-similar nature of ethernet traffic (extended version) [J]. IEEE/ACM Transactions on Networking, 1994, 2(1): 1–15.
Paxson V, Floyd S. Wide area traffic: The failure of poisson modeling [J]. IEEE/ACM Transactions on Networking, 1995, 3(3): 226–244.
Tickoo O, Sikdar B. On the impact of IEEE 802.11 MAC on traffic characteristics [J]. IEEE Journal on Selected Areas in Communications, 2003, 21(2): 189–203.
Li M, Lim S C. Modeling network traffic using generalized Cauchy process [J]. Physica A, 2008, 387: 2584–2594.
Riedi R H, Crouse M S, Ribeiro V J, et al. A multifractal wavelet model with application to network traffic [J]. IEEE Special Issue on Information Theory, 1999, 45: 992–1018.
Veitch D, Abry P. A statistical test for the time constancy of scaling exponents [J]. IEEE Transactions on Signal Processing, 2001, 49: 2325–2334.
Stoev S, Taqqu M, Park C, et al. On the wavelet spectrum diagnostic for Hurst parameter estimation in the analysis of Internet traffic [J]. Computer Networks, 2005, 48(3): 423–445.
Patrice A, Darryl V. Wavelet analysis of long-range-dependence traffic [J]. IEEE Transactions on Information Theory, 1998, 44(1): 2–15.
David R, Sebastia S. On-line segmentation of nonstationary fractal network traffic with wavelet transforms and log-likelihood-based statistics [J]. Lecture Notes in Computer Sciences, 2005, 3375: 280–283.
David R, Flaminio M, Sebastia S. Segmenting LRD traffic with wavelets and the Schwarz information criterion [C]// Proceedings of the 2006 Passive and Active Measurements Conference. Adelaide, Australia: IEEE Press, 2006: 121–130.
Li M. Error order of magnitude for modeling autocorrelation function of interarrival times of network traffic using fractional Gaussian noise [C]// 7th WSEAS International Conference on Applied Computer and Applied Computational Science. Venice, Italy: IEEE Press, 2008: 167–172.
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item: the National High Technology Research and Development Program (863) of China (Nos. 2005AA145110 and 2006AA01Z436), the Natural Science Foundation of Shanghai of China (No. 05ZR14083), and the Pudong New Area Technology Innovation Public Service Platform of China (No. PDPT2005-04)
Rights and permissions
About this article
Cite this article
Xia, Zm., Lu, Sn., Li, Jh. et al. On-line modeling of non-stationary network traffic with schwarz information criterion. J. Shanghai Jiaotong Univ. (Sci.) 15, 213–217 (2010). https://doi.org/10.1007/s12204-010-9515-6
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12204-010-9515-6
Key words
- network traffic model
- self-similarity
- Schwarz information criterion (SIC)
- discrete wavelet transform (DWT)
- fractional Gaussian noise (FGN)