Optimization algorithms for energy-efficient data centers
Hendrik F. Hamann
InterPACK 2013
We apply Bayesian reasoning techniques to perform fault localization in complex communication systems while using dynamic, ambiguous, uncertain, or incorrect information about the system structure and state. We introduce adaptations of two Bayesian reasoning techniques for polytrees, iterative belief updating, and iterative most probable explanation. We show that these approximate schemes can be applied to belief networks of arbitrary shape and overcome the inherent exponential complexity associated with exact Bayesian reasoning. We show through simulation that our approximate schemes are almost optimally accurate, can identify multiple simultaneous faults in an event driven manner, and incorporate both positive and negative information into the reasoning process. We show that fault localization through iterative belief updating is resilient to noise in the observed symptoms and prove that Bayesian reasoning can now be used in practice to provide effective fault localization. © 2004 IEEE.
Hendrik F. Hamann
InterPACK 2013
Sai Zeng, Angran Xiao, et al.
CAD Computer Aided Design
M.J. Slattery, Joan L. Mitchell
IBM J. Res. Dev
Michael D. Moffitt
ICCAD 2009