Table of Contents Author Guidelines Submit a Manuscript
Journal of Probability and Statistics
Volume 2010, Article ID 375942, 16 pages
http://dx.doi.org/10.1155/2010/375942
Research Article

An Effective Method of Monitoring the Large-Scale Traffic Pattern Based on RMT and PCA

1Research Division, China Mobile Group Design Institute Co. Ltd, Beijing 100080, China
2Department of Electronic Engineering, Tsinghua University, Beijing 10084, China

Received 2 September 2009; Revised 17 January 2010; Accepted 12 April 2010

Academic Editor: Chunsheng Ma

Copyright © 2010 Jia Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Greenberg, G. Hjalmtysson, D. A. Maltz et al., “A clean slate 4D approach to network control and management,” ACM SIGCOMM Computer Communication Review, vol. 35, no. 5, pp. 41–54, 2005. View at Publisher · View at Google Scholar · View at Scopus
  2. F. Hao, M. Kodialam, T. V. Lakshman, and S. Mohanty, “Fast, memory efficicent flow rate estimation using runs,” IEEE/ACM Transactions on Networking, vol. 15, no. 6, pp. 1467–1477, 2007. View at Google Scholar
  3. G. Junwei and Z. Guofeng, “Traffic measurement for traffic engineering in IP/MPLS based telecom core networks,” The Journal China Universities of Posts and Telecommunications, vol. 9, no. 2, 2002. View at Google Scholar
  4. R. Kawahara, T. Mori, N. Kamiyama, S. Harada, and S. Asano, “A study on detecting network anomalies using sampled flow statistics,” in International Symposium on Applications and the Internet Workshops (SAINT '07), Hiroshima, Japan, January 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. L. Laloux, P. Cizeau, J.-P. Bouchaud, and M. Potters, “Noise dressing of financial correlation matrices,” Physical Review Letters, vol. 83, no. 7, pp. 1467–1470, 1999. View at Google Scholar · View at Scopus
  6. V. Plerou, P. Gopikrishnan, B. Rosenow, L. A. N. Amaral, and H. E. Stanley, “Universal and nonuniversal properties of cross correlaitons in financial time series,” Physics Review Letter, vol. 83, no. 7, pp. 1471–1474, 1999. View at Google Scholar
  7. F. J. Dyson, “Statistical theory of the energy levels of complex systems. I,” Journal of Mathematical Physics, vol. 3, pp. 140–156, 1962. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  8. M. Barthélemy, B. Gondran, and E. Guichard, “Large scale cross-correlations in Internet traffic,” Physical Review E, vol. 66, no. 5, Article ID 056110, pp. 1–7, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Yuan and K. Mills, “Monitoring the macroscopic effect of DDoS flooding attacks,” IEEE Transactions on Dependable and Secure Computing, vol. 2, no. 4, pp. 324–335, 2005. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Liu, W. Zhang, J. Yuan, D. Jin, and L. Zeng, “Monitoring the spatial-temporal effect of internet traffic based on random matrix theory,” in Proceedings of the 33rd Conference on Local Computer Networks (LCN '08), pp. 258–265, Montreal, AB, Canada, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Claypool, R. Kinicki, M. Li, J. Nichols, and H. Wu, “Inferring queue sizes in access networks by active measurement,” in Proceedings of the Passive and Active Measurement (PAM '08), April 2004.
  12. M. Weigle, K. Jeffay, and F. D. Smith, “Quantifying the effects of recent protocol improvements to standards-track TCP,” in Proceedings of the 11th IEEE/ACM Int'l Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication, 2003.
  13. A. Feldmann, A. C. Gilbert, W. Willinger, and T. G. Kurtz, “The changing nature of network traffic: scaling phenomena,” Computer Communication Review, vol. 28, no. 2, pp. 5–29, 1998. View at Google Scholar · View at Scopus
  14. E. P. Wigner, “On the statistical distribution of the widths and spacings of nuclear resonance levels,” Mathematical Proceedings of the Cambridge Philosophical Society, pp. 790–798, 1951. View at Google Scholar
  15. E. P. Wigner, “On the statistical distribution of the widths and spacings of nuclear resonance levels,” Mathematical Proceedings of the Cambridge Philosophical Society, vol. 47, pp. 790–798, 1951. View at Google Scholar
  16. E. P. Wigner, “Results and theory of resonance absorption,” in Proceedings of the Conference on Neutron Physics by Time-of-Flight, pp. 59–70, Gatlinburg, Tenn, USA, 1956.
  17. V. Plerou, P. Gopikrishnan, B. Rosenow, L. A.N. Amaral, and H. E. Stanley, “Universal and nonuniversal properties of cross correlations in financial time series,” Physical Review Letters, vol. 83, no. 7, pp. 1471–1474, 1999. View at Google Scholar · View at Scopus
  18. A. Feldmann, A. C. Gilbert, P. Huang, and W. Willinger, “Dynamics of IP traffic: a study of the role of variability and the impact of control,” in Proceedings of the ACM SIGCOMM Conference, pp. 301–313, Boston, Mass, USA, 1999.
  19. A. M. Sengupta and P. P. Mitra, “Distributed of singular values for some random matrices,” Physics Review E, vol. 60, no. 3, pp. 3389–3392, 1999. View at Google Scholar
  20. I. T. Jolliffe, Principal Component Analysis, Springer Series in Statistics, Springer, New York, NY, USA, 2nd edition, 2002. View at MathSciNet
  21. J. Cadima, J. O. Cerdeira, and M. Minhoto, “Computational aspects of algorithms for variable selection in the context of principal components,” Computational Statistics and Data Analysis, vol. 47, no. 2, pp. 225–236, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  22. G. P. McCabe, “Principal variables,” Technometrics, vol. 26, no. 2, pp. 137–144, 1984. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet