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      Shifting network tomography toward a practical goal

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      Author
      Ghita, D.
      Karakuş, Can
      Argyraki, K.
      Thiran, P.
      Date
      2011
      Source Title
      Proceedings of the 7th International Conference on Emerging Networking Experiments and Technologies, CoNEXT 2011
      Publisher
      ACM
      Language
      English
      Type
      Conference Paper
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      Abstract
      Boolean Inference makes it possible to observe the congestion status of end-to-end paths and infer, from that, the congestion status of individual network links. In principle, this can be a powerful monitoring tool, in scenarios where we want to monitor a network without having direct access to its links. We consider one such real scenario: a Tier-1 ISP operator wants to monitor the congestion status of its peers. We show that, in this scenario, Boolean Inference cannot be solved with enough accuracy to be useful; we do not attribute this to the limitations of particular algorithms, but to the fundamental difficulty of the Inference problem. Instead, we argue that the "right" problem to solve, in this context, is compute the probability that each set of links is congested (as opposed to try to infer which particular links were congested when). Even though solving this problem yields less information than provided by Boolean Inference, we show that this information is more useful in practice, because it can be obtained accurately under weaker assumptions than typically required by Inference algorithms and more challenging network conditions (link correlations, non-stationary network dynamics, sparse topologies).
      Keywords
      Congestion status
      Individual network
      Inference algorithm
      Inference problem
      Monitoring tools
      Network condition
      Network dynamics
      Network tomography
      Nonstationary
      Algorithms
      Inference engines
      Permalink
      http://hdl.handle.net/11693/28266
      Published Version (Please cite this version)
      https://doi.org/10.1145/2079296.2079320
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      • Department of Electrical and Electronics Engineering 3601
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