• DocumentCode
    1315555
  • Title

    Model-Based Identification of Dominant Congested Links

  • Author

    Wei, Wei ; Wang, Bing ; Towsley, Don ; Kurose, Jim

  • Author_Institution
    Comput. Sci. Dept., Univ. of Massachusetts Amherst, Amherst, MA, USA
  • Volume
    19
  • Issue
    2
  • fYear
    2011
  • fDate
    4/1/2011 12:00:00 AM
  • Firstpage
    456
  • Lastpage
    469
  • Abstract
    In this paper, we propose a model-based approach that uses periodic end-end probes to identify whether a “dominant congested link” exists along an end-end path. Informally, a dominant congested link refers to a link that incurs the most losses and significant queuing delays along the path. We begin by providing a formal yet intuitive definition of dominant congested link and present two simple hypothesis tests to identify whether such a link exists. We then present a novel model-based approach for dominant congested link identification that is based on interpreting probe loss as an unobserved (virtual) delay. We develop parameter inference algorithms for hidden Markov model (HMM) and Markov model with a hidden dimension (MMHD) to infer this virtual delay. Our validation using ns simulation and Internet experiments demonstrate that this approach can correctly identify a dominant congested link with only a small amount of probe data. We further provide an upper bound on the maximum queuing delay of the dominant congested link once we identify that such a link exists.
  • Keywords
    Internet; hidden Markov models; queueing theory; telecommunication links; HMM; Internet experiments; MMHD; Markov model with a hidden dimension; dominant congested links; hidden Markov model; hypothesis tests; model-based identification; ns simulation; parameter inference algorithms; periodic end-end probes; queuing delays; virtual delay; Bandwidth; Delay; Hidden Markov models; Internet; Loss measurement; Probes; Upper bound; Bottleneck link; Markov model with a hidden dimension (MMHD); dominant congested link; end–end inference; hidden Markov model (HMM); network inference; network management; path characteristics;
  • fLanguage
    English
  • Journal_Title
    Networking, IEEE/ACM Transactions on
  • Publisher
    ieee
  • ISSN
    1063-6692
  • Type

    jour

  • DOI
    10.1109/TNET.2010.2068058
  • Filename
    5565532