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For system diagnosis, Lai et al.16 introduced a new measurement, called the conditional diagnosability, by adding a condition that no faulty set contains all the neighbors of any vertex in a network. Taking the hypercube as the target, Lai et al.16 (respectively, Hsu et al.13) estimated the PMC-based19 (respectively, the comparison-based18) conditional diagnosability as about four (respectively, three) times larger than the original diagnosability. In this paper, we extend the concept of conditional diagnosability to the generalized version of hypercubes, the class of hypercube-like networks. We prove that the conditional diagnosability of an n-dimensional hypercube-like network HLn is 3n - 5 under the comparison diagnosis model, for n ≥ 5. [ABSTRACT FROM AUTHOR]

Abstract: Processor fault diagnosis plays an important role in multiprocessor systems for reliable computing, and the diagnosability of many well-known networks has been explored. Lai et al. proposed a novel measure of diagnosability, called conditional diagnosability, by adding an additional condition that any faulty set cannot contain all the neighbors of any vertex in a system. We make a contribution to the evaluation of diagnosability for hypercube networks under the comparison model and prove that the conditional diagnosability of n-dimensional Hypercube Q n is 3(n −2)+1 for n ⩾5. The conditional diagnosability of Q n is about three times larger than the classical diagnosability of Q n . [Copyright &y& Elsevier]