The missing piece: a distributed system-level diagnosis model for the implementation of unreliable failure detectors

Duarte, Elias P.; Rodrigues, Luiz A.; Camargo, Edson T.; Turchetti, Rogério C.

doi:10.1007/s00607-023-01211-8

Cited by 4 publications

(1 citation statement)

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“…These areas of study may help to provide a more comprehensive approach to fault diagnosis and improve the overall performance of the system. Finally, before concluding this article, we provide some valuable references related to system diagnosis for further exploration [35][36][37].…”

Section: Discussionmentioning

confidence: 99%

An Improved Fault Diagnosis Algorithm for Highly Scalable Data Center Networks

Lin,

Li,

Chang

et al. 2024

Mathematics

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Fault detection and localization are vital for ensuring the stability of data center networks (DCNs). Specifically, adaptive fault diagnosis is deemed a fundamental technology in achieving the fault tolerance of systems. The highly scalable data center network (HSDC) is a promising structure of server-centric DCNs, as it exhibits the capacity for incremental scalability, coupled with the assurance of low cost and energy consumption, low diameter, and high bisection width. In this paper, we first determine that both the connectivity and diagnosability of the m-dimensional complete HSDC, denoted by HSDCm(m), are m. Further, we propose an efficient adaptive fault diagnosis algorithm to diagnose an HSDCm(m) within three test rounds, and at most N+4m(m−2) tests with m≥3 (resp. at most nine tests with m=2), where N=m·2m is the total number of nodes in HSDCm(m). Our experimental outcomes demonstrate that this diagnosis scheme of HSDC can achieve complete diagnosis and significantly reduce the number of required tests.

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Section: Discussionmentioning

confidence: 99%