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The 1-good-neighbor diagnosability of unidirectional hypercubes under the PMC model

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  • Lin, Shangwei
  • Zhang, Wenli

Abstract

The hypercubes are a famous class of networks for multiprocessor systems and the unidirectional hypercubes are hypercube interconnection topologies with simplex unidirectional links. Under the classic PMC model, each processor in a multiprocessor system tests a subset of its neighbors. The collection of tests in this system can be modeled by a directed graph. The diagnosability of a system is the maximum number of faulty processors that the system may identify according to the outcomes of the tests, and the g-good-neighbor diagnosability is a more accurate indicator than the diagnosability. In this paper, we first determine the 1-good-neighbor connectivity of unidirectional hypercubes and then determine the diagnosability and 1-good-neighbor diagnosability of hypercube networks when unidirectional hypercubes are used as the collection of tests under the PMC model.

Suggested Citation

  • Lin, Shangwei & Zhang, Wenli, 2020. "The 1-good-neighbor diagnosability of unidirectional hypercubes under the PMC model," Applied Mathematics and Computation, Elsevier, vol. 375(C).
    • Handle: RePEc:eee:apmaco:v:375:y:2020:i:c:s0096300320300606
    DOI: 10.1016/j.amc.2020.125091
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    References listed on IDEAS

    as
    1. Wang, Shiying & Ma, Xiaolei, 2018. "The g-extra connectivity and diagnosability of crossed cubes," Applied Mathematics and Computation, Elsevier, vol. 336(C), pages 60-66.
    2. Wang, Shiying & Yang, Yuxing, 2017. "The 2-good-neighbor (2-extra) diagnosability of alternating group graph networks under the PMC model and MM* model," Applied Mathematics and Computation, Elsevier, vol. 305(C), pages 241-250.
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