The Comparison Approach to Multiprocessor Fault Diagnosis
IEEE Transactions on Computers
Undirected Graph Models for System-Level Fault Diagnosis
IEEE Transactions on Computers
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A comparison connection assignment for diagnosis of multiprocessor systems
ISCA '80 Proceedings of the 7th annual symposium on Computer Architecture
Graph Theory With Applications
Graph Theory With Applications
The consensus problem in fault-tolerant computing
ACM Computing Surveys (CSUR)
Probabilistic diagnosis of multiprocessor systems
ACM Computing Surveys (CSUR)
On Parallel Algorithms for Single-Fault Diagnosis in Fault Propagation Graph Systems
IEEE Transactions on Parallel and Distributed Systems
Globally Optimal Diagnosis in Systems with Random Faults
IEEE Transactions on Computers
Optimal Diagnosis of Heterogeneous Systems with Random Faults
IEEE Transactions on Computers
Efficient Comparison-Based Fault Diagnosis of Multiprocessor Systems Using Genetic Algorithms
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Reliable Fault Diagnosis with Few Tests
Combinatorics, Probability and Computing
TAMC'07 Proceedings of the 4th international conference on Theory and applications of models of computation
An exact algorithm based on chain implication for the Min-CVCB problem
COCOA'07 Proceedings of the 1st international conference on Combinatorial optimization and applications
Error-free multi-valued consensus with byzantine failures
Proceedings of the 30th annual ACM SIGACT-SIGOPS symposium on Principles of distributed computing
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The authors consider a comparison-based probabilistic model for multiprocessor fault diagnosis. They study the problem of optimal diagnosis, which is to correctly identify the status (faulty/fault-free) of units in the system, with maximum probability. For some parameter values, this probabilistic model is well approximated by the asymmetric comparison model introduced by M. Malek (1980). For arbitrary systems it is shown that optimal diagnosis in the probabilistic model and in Malek's model is NP-hard. However, the authors construct efficient diagnosis algorithms in the asymmetric comparison model for a class of systems corresponding to bipartite graphs which includes hypercubes, grids, and forests. Furthermore, for ring systems, a linear-time algorithm to perform optimal diagnosis in the probabilistic model is presented.