A Fast Pessimistic Diagnosis Algorithm for Hypercube-Like Networks under the Comparison Model

Diagnosis by comparison is a realistic approach to detect faults of multiprocessor systems. This paper considers a pessimistic diagnostic strategy for hypercube-like multiprocessor systems under the comparison model. The pessimistic strategy is a diagnostic process whereby all faulty nodes can be correctly identified and at most one fault-free node may be misjudged as a faulty node. We propose a pessimistic diagnosis algorithm based on the largest component in the faulty system. For a system with
$N=2^n$
nodes and
$n\ge 5$
, when the number of faulty nodes is bounded by
$2n-2$
, the algorithm can correctly identify all nodes except at most one node left undiagnosed. The time complexity of the algorithm is
$O(Nlog_2N)$
.