r-component diagnosability of hypercubes under the PMC model

Fault diagnosability evaluation for interconnection networks is important to the design and maintenance of multiprocessor systems. Many parameters have been proposed to evaluate the fault diagnosis capability of interconnection networks, such as conditional diagnosability, h-extra conditional diagnosability, and g-good neighbor diagnosability. Recently Zhang et al. [1] proposed r-component diagnosability as a measure of the fault diagnosis capability of interconnection networks in circumstances with many faults, and they also determined the r-component diagnosability of hypercubes under both the PMC and MM* models for 2 <= r <= n + 1 and n >= 7. In this paper, we extend their results by determining the r-component diagnosability of hypercubes under the PMC model for n + 2 <= r <= 2n - 5 and n >= 7. We also explore the relationship between the component diagnosability of hypercubes under the PMC model and its component connectivity. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper highlights the importance of fault diagnosability evaluation for interconnection networks and introduces various parameters to assess the fault diagnosis capability. The authors also extend previous research by determining the r-component diagnosability of hypercubes under the PMC model and studying its relationship with component connectivity.