Robust network traffic estimation via sparsity and low rank

Morteza Mardani, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations

Abstract

Accurate estimation of origin-to-destination (OD) traffic flows provides valuable input for network management tasks. However, lack of flow-level observations as well as intentional and unintentional anomalies pose major challenges toward achieving this goal. Leveraging the low intrinsic- dimensionality of OD flows and the sparse nature of anomalies, this paper proposes a convex program with nuclear-norm and ℓ1-norm regularization terms to estimate the nominal and anomalous traffic components, using a small subset of (possibly anomalous) flow counts in addition to link counts. Analysis and simulations confirm that the said estimator can exactly recover sufficiently low-dimensional nominal traffic and sparse enough anomalies when the routing matrix is column-incoherent, and an adequate amount of flow counts are randomly sampled. The results offer valuable insights about the measurement types and network scenaria giving rise to accurate traffic estimation. Tests with real Internet data corroborate the effectiveness of the novel estimator.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages4529-4533
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

Keywords

  • Sparsity
  • low rank
  • traffic estimation

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