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

Wang, Xiangke; You, Yudu; Li, Xiaoyan; Liu, Ximeng; Yang, Yang

doi:10.1007/978-981-16-7913-1_4

Cited by 1 publication

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper is an extended research of [8], which discusses a high scalability data center network (HSDC for short), a server-centric DCN known for its desirable features, such as incremental scalability, cost-efficiency, low energy consumption, short diameter, and high bisection width. For a server-centric DCN, each switch can be considered a transparent device so that we can define the logic graph of the HSDC as L-HSDC concerning the connection among servers.…”

Section: Introductionmentioning

confidence: 99%

An Improved Fault Diagnosis Algorithm for Highly Scalable Data Center Networks

Lin,

Li,

Chang

et al. 2024

Mathematics

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%