A Systematic Algorithm for Identifying Faults on Hypercube-Like Networks Under the Comparison Model

There is a growing demand for fault diagnosis to increase the reliability of systems. Diagnosis by comparison is a realistic approach to the fault diagnosis of multiprocessor systems. In this paper, we consider
n
-dimensional hypercube-like networks for
n
≥ 5. We propose an efficient fault diagnosis algorithm for
n
-dimensional hypercube-like networks under the MM comparison model by exploiting the Hamiltonian and extended-star properties. Applying our algorithm, the faulty processors in
n
-dimensional hypercubes,
n
-dimensional crossed cubes,
n
-dimensional twisted cubes, and
n
-dimensional Möbius cubes can all be diagnosed in linear time provided the number of faulty processors is not more than the dimension
n
.