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Efficient Fault Detection and Diagnosis in Complex Software Systems with Information-Theoretic Monitoring
July/August 2011 (vol. 8 no. 4)
pp. 510-522
ASCII Text
x 
 
Miao Jiang, Mohammad A. Munawar, Thomas Reidemeister, Paul A.S. Ward, "Efficient Fault Detection and Diagnosis in Complex Software Systems with Information-Theoretic Monitoring," IEEE Transactions on Dependable and Secure Computing, vol. 8, no. 4, pp. 510-522, July/August, 2011.
 
 
BibTex
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@article{ 10.1109/TDSC.2011.16,
author = {Miao Jiang and Mohammad A. Munawar and Thomas Reidemeister and Paul A.S. Ward},
title = {Efficient Fault Detection and Diagnosis in Complex Software Systems with Information-Theoretic Monitoring},
journal ={IEEE Transactions on Dependable and Secure Computing},
volume = {8},
number = {4},
issn = {1545-5971},
year = {2011},
pages = {510-522},
doi = {http://doi.ieeecomputersociety.org/10.1109/TDSC.2011.16},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Dependable and Secure Computing
TI - Efficient Fault Detection and Diagnosis in Complex Software Systems with Information-Theoretic Monitoring
IS - 4
SN - 1545-5971
SP510
EP522
EPD - 510-522
A1 - Miao Jiang,
A1 - Mohammad A. Munawar,
A1 - Thomas Reidemeister,
A1 - Paul A.S. Ward,
PY - 2011
KW - Self-managing systems
KW - fault detection
KW - fault diagnosis
KW - information theory
KW - mutual information
KW - autonomic systems.
VL - 8
JA - IEEE Transactions on Dependable and Secure Computing
ER -
 
Miao Jiang, University of Waterloo, Waterloo
Mohammad A. Munawar, University of Waterloo, Waterloo
Thomas Reidemeister, University of Waterloo, Waterloo
Paul A.S. Ward, University of Waterloo, Waterloo
Management metrics of complex software systems exhibit stable correlations which can enable fault detection and diagnosis. Current approaches use specific analytic forms, typically linear, for modeling correlations. In practice, more complex nonlinear relationships exist between metrics. Moreover, most intermetric correlations form clusters rather than simple pairwise correlations. These clusters provide additional information and offer the possibility for optimization. In this paper, we address these issues by using Normalized Mutual Information (NMI) as a similarity measure to identify clusters of correlated metrics, without assuming any specific form for the metric relationships. We show how to apply the Wilcoxon Rank-Sum test on the entropy measures to detect errors in the system. We also present three diagnosis algorithms to locate faulty components: RatioScore, based on the Jaccard coefficient, SigScore, which incorporates knowledge of component dependencies, and BayesianScore, which uses Bayesian inference to assign a fault probability to each component. We evaluate our approach in the context of a complex enterprise application, and show that 1) stable, nonlinear correlations exist and can be captured with our approach; 2) we can detect a large fraction of faults with a low false positive rate (we detect up to 18 of the 22 faults we injected); and 3) we improve the diagnosis with our new diagnosis algorithms.

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Index Terms:
Self-managing systems, fault detection, fault diagnosis, information theory, mutual information, autonomic systems.
Citation:
Miao Jiang, Mohammad A. Munawar, Thomas Reidemeister, Paul A.S. Ward, "Efficient Fault Detection and Diagnosis in Complex Software Systems with Information-Theoretic Monitoring," IEEE Transactions on Dependable and Secure Computing, vol. 8, no. 4, pp. 510-522, July-Aug. 2011, doi:10.1109/TDSC.2011.16
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