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author:

Lin, Wanling (Lin, Wanling.) [1] | Zhuang, Hongbin (Zhuang, Hongbin.) [2] | Li, Xiao-Yan (Li, Xiao-Yan.) [3] | Zhang, Yufang (Zhang, Yufang.) [4]

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Abstract:

In the realm of multiprocessor systems, the evaluation of interconnection network reliability holds utmost significance, both in terms of design and maintenance. The intricate nature of these systems calls for a systematic assessment of reliability metrics, among which, two metrics emerge as vital: connectivity and diagnosability. The Rg-conditional connectivity is the minimum number of processors whose deletion will disconnect the multiprocessor system and every processor has at least g fault-free neighbors. The Rg-conditional diagnosability is a novel generalized conditional diagnosability, which is the maximum number of faulty processors that can be identified under the condition that every processor has no less than g fault-free neighbors. In this paper, we first investigate the Rg-conditional connectivity of generalized exchanged X-cubes GEX(s,t) and present the lower (upper) bounds of the Rg-conditional diagnosability of GEX(s,t) under the PMC model. Applying our results, the Rg-conditional connectivity and the lower (upper) bounds of Rg-conditional diagnosability of generalized exchanged hypercubes, generalized exchanged crossed cubes, and locally generalized exchanged twisted cubes under the PMC model are determined. Our comparative analysis highlights the superiority of Rg-conditional diagnosability, showcasing its effectiveness in guiding reliability studies across a diverse set of networks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

Keyword:

Fault tolerance Geometry Interconnection networks (circuit switching) Multiprocessing systems Reliability analysis

Community:

  • [ 1 ] [Lin, Wanling]College of Computer and Data Science, Fuzhou University, Fuzhou; 350108, China
  • [ 2 ] [Lin, Wanling]Fujian Provincial Key Laboratory of Information Security of Network Systems, Fuzhou University, Fuzhou; 350108, China
  • [ 3 ] [Zhuang, Hongbin]College of Computer and Data Science, Fuzhou University, Fuzhou; 350108, China
  • [ 4 ] [Zhuang, Hongbin]Fujian Provincial Key Laboratory of Information Security of Network Systems, Fuzhou University, Fuzhou; 350108, China
  • [ 5 ] [Li, Xiao-Yan]College of Computer and Data Science, Fuzhou University, Fuzhou; 350108, China
  • [ 6 ] [Li, Xiao-Yan]Fujian Provincial Key Laboratory of Information Security of Network Systems, Fuzhou University, Fuzhou; 350108, China
  • [ 7 ] [Zhang, Yufang]College of Computer and Data Science, Fuzhou University, Fuzhou; 350108, China
  • [ 8 ] [Zhang, Yufang]Fujian Provincial Key Laboratory of Information Security of Network Systems, Fuzhou University, Fuzhou; 350108, China

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Source :

Journal of Supercomputing

ISSN: 0920-8542

Year: 2024

Issue: 8

Volume: 80

Page: 11401-11430

2 . 5 0 0

JCR@2023

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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