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Empirical Study of Topology Effects on Diagnosis in Computer Networks | IEEE Conference Publication | IEEE Xplore

Empirical Study of Topology Effects on Diagnosis in Computer Networks


Abstract:

In this paper, we compare the efficiency of fault detection and diagnosis in networks having different topological properties, such as scale-free networks and Erdos-Renyi...Show More

Abstract:

In this paper, we compare the efficiency of fault detection and diagnosis in networks having different topological properties, such as scale-free networks and Erdos-Renyi random graphs. Efficiency measures include both the number of tests (e.g., end-to-end network probes) necessary for diagnosis and the computational complexity of diagnosis. We observe that diagnosis in scale-free networks typically requires significantly larger number of tests than diagnosis in random networks. However, the computational complexity of diagnosis appears to be much lower for scale-free networks since the corresponding Bayesian network models used for probabilistic diagnosis tend to have much lower induced width - a topological parameter controlling the complexity of inference in Bayesian networks. We believe that our observations provide important insights for design and deployment of cost-efficient diagnostic methods in computer networks and distributed systems.
Date of Conference: 08-11 October 2007
Date Added to IEEE Xplore: 14 January 2008
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Conference Location: Pisa, Italy

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