Three Round Adaptive Diagnosis in Hierarchical Multiprocessor Systems

Lai, Pao-Lien; Chiu, Ming-Yi; Tsai, Chang-Hsiung

doi:10.1109/tr.2013.2270411

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…For example, in a completely connected system, it has been demonstrated that the number of tests required and deemed adequate to identify at most t faulty processors decreases from Nt to N + t − 1 under the PMC model [21]. In addition, several well-known adaptive diagnosis algorithms for interconnected network topologies have been investigated, such as hypercube networks [22,23], hierarchical multiprocessor systems [24], butterfly networks [25], and Hamiltonian networks [26]. However, the existing fault diagnosis schemes for DCNs generally lack adaptability and exhibit low diagnostic efficiency [27][28][29].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

An Improved Fault Diagnosis Algorithm for Highly Scalable Data Center Networks

Lin,

Li,

Chang

et al. 2024

Mathematics

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“…The flexibility of this method increases the efficiency of the diagnosis. For adaptive diagnosis, many interconnection networks have been studied, such as butterfly networks [26], hypercubes [5,6,12], twisted cubes [27], hierarchical crossed cube [14], and dual cube [2]. Previous studies have demonstrated that, while locating f < n/2 faults requires worst-case time of at least f in the nonadaptive setting, adaptive diagnosis can locate less than n/2 faults among n in constant time.…”

Section: Introductionmentioning

confidence: 99%

Hamiltonian cycle embedding with fault-tolerant edges and adaptive diagnosis in half hypercube

Fan,

Liu,

2023

J Supercomput

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Interconnection network directly affects the efficiency of parallel and distributed systems. Hypercube is one of the most important interconnection networks in parallel computing systems. As a significant variant of hypercube, half hypercube has the same number of vertices as hypercube while its degree is only half that of hypercube, which indicates that it has lower network overhead than hypercube. In this paper, we study the fault-tolerant embedding of Hamiltonian cycles and design an adaptive diagnosis algorithm for the half hypercube. Firstly, we prove that the half hypercube is Hamiltonian and propose an algorithm to construct a Hamiltonian cycle in the network. Furthermore, we prove that half hypercube is Hamiltonian with no more than (⌈n/2⌉−1) faulty edges. Finally, we present a parallel adaptive diagnosis scheme under PMC model, which can identify almost all faulty vertices in five rounds.

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An Efficient Adaptive Fault Diagnosis Algorithm for Highly Scalable Data Center Networks

Wang

You

et al. 2021

Communications in Computer and Information Science

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Three Round Adaptive Diagnosis in Hierarchical Multiprocessor Systems

Cited by 8 publications

References 25 publications

An Improved Fault Diagnosis Algorithm for Highly Scalable Data Center Networks

An Improved Fault Diagnosis Algorithm for Highly Scalable Data Center Networks

Hamiltonian cycle embedding with fault-tolerant edges and adaptive diagnosis in half hypercube

An Efficient Adaptive Fault Diagnosis Algorithm for Highly Scalable Data Center Networks

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