Check out the new USENIX Web site.

USENIX Home . About USENIX . Events . membership . Publications . Students
IMC '05, 2005 Internet Measurement Conference — Abstract

Pp. 253–266 of the Proceedings

Sparse Approximations for High Fidelity Compression of Network Traffic Data

William Aiello, University of British Columbia; Anna Gilbert, University of Michigan; Brian Rexroad, AT&T Labs; Vyas Sekar, Carnegie Mellon University


An important component of traffic analysis and network monitoring is the ability to correlate events across multiple data streams, from different sources and from different time periods. Storing such a large amount of data for visualizing traffic trends and for building prediction models of ``normal'' network traffic represents a great challenge because the data sets are enormous. In this paper we present the application and analysis of signal processing techniques for effective practical compression of network traffic data. We propose to use a sparse approximation of the network traffic data over a rich collection of natural building blocks, with several natural dictionaries drawn from the networking community's experience with traffic data. We observe that with such natural dictionaries, high fidelity compression of the original traffic data can be achieved such that even with a compression ratio of around 1:6, the compression error, in terms of the energy of the original signal lost, is less than 1%. We also observe that the sparse representations are stable over time, and that the stable components correspond to well-defined periodicities in network traffic.
  • View the full text of this paper in HTML and PDF.
    The Proceedings are published as a collective work, © 2005 by the USENIX Association. All Rights Reserved. Rights to individual papers remain with the author or the author's employer. Permission is granted for the noncommercial reproduction of the complete work for educational or research purposes. USENIX acknowledges all trademarks within this paper.

  • If you need the latest Adobe Acrobat Reader, you can download it from Adobe's site.

?Need help? Use our Contacts page.

Last changed: 24 Oct. 2005 rc
IMC '05 Tech Sessions
IMC '05 Home