Probabilistic Fault Diagnosis Using Adaptive Probing

  • Maitreya Natu
  • Adarshpal S. Sethi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4785)


Past research on probing-based network monitoring provides solutions based on preplanned probing which is computationally expensive, is less accurate, and involves a large management traffic. Unlike preplanned probing, adaptive probing proposes to select probes in an interactive manner sending more probes to diagnose the observed problem areas and less probes in the healthy areas, thereby significantly reducing the number of probes required. Another limitation of most of the work proposed in the past is that it assumes a deterministic dependency information between the probes and the network components. Such an assumption can not be made when complete and accurate network information might not be available. Hence, there is a need to develop network monitoring algorithms that can localize failures in the network even in the presence of uncertainty in the inferred dependencies between probes and network components. In this paper, we propose a fault diagnosis tool with following novel features: (1) We present an adaptive probing based solution for fault diagnosis which is cost-effective, failure resistant, more accurate, and involves less management traffic as compared to the preplanned probing approach. (2) We address the issues that arise with the presence of a non-deterministic environment and present probing algorithms that consider the involved uncertainties in the collected network information.


Probe Selection Fault Diagnosis Fault Localization Failure Detection Node Failure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© IFIP International Federation for Information Processing 2007

Authors and Affiliations

  • Maitreya Natu
    • 1
  • Adarshpal S. Sethi
    • 1
  1. 1.Dept. of Computer and Information Science, University of Delaware, Newark, DE, 19716USA

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