IDEAS home Printed from https://ideas.repec.org/a/hin/jnljps/375942.html
   My bibliography  Save this article

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

Author

Listed:
  • Jia Liu
  • Peng Gao
  • Jian Yuan
  • Xuetao Du

Abstract

Mechanisms to extract the characteristics of network traffic play a significant role in traffic monitoring, offering helpful information for network management and control. In this paper, a method based on Random Matrix Theory (RMT) and Principal Components Analysis (PCA) is proposed for monitoring and analyzing large-scale traffic patterns in the Internet. Besides the analysis of the largest eigenvalue in RMT, useful information is also extracted from small eigenvalues by a method based on PCA. And then an appropriate approach is put forward to select some observation points on the base of the eigen analysis. Finally, some experiments about peer-to-peer traffic pattern recognition and backbone aggregate flow estimation are constructed. The simulation results show that using about 10% of nodes as observation points, our method can monitor and extract key information about Internet traffic patterns.

Suggested Citation

  • Jia Liu & Peng Gao & Jian Yuan & Xuetao Du, 2010. "An Effective Method of Monitoring the Large-Scale Traffic Pattern Based on RMT and PCA," Journal of Probability and Statistics, Hindawi, vol. 2010, pages 1-16, June.
    • Handle: RePEc:hin:jnljps:375942
    DOI: 10.1155/2010/375942
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/JPS/2010/375942.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/JPS/2010/375942.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2010/375942?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnljps:375942. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.