An Improved Fault Diagnosis Algorithm for Highly Scalable Data Center Networks

Wanling Lin, Xiao-Yan Li (), Jou-Ming Chang () and Xiangke Wang
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Wanling Lin: College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
Xiao-Yan Li: College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
Jou-Ming Chang: Institute of Information and Decision Sciences, National Taipei University of Business, Taipei 10051, Taiwan
Xiangke Wang: College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China

Mathematics, 2024, vol. 12, issue 4, 1-16

Abstract: 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 H S D C m ( m ) , are m . Further, we propose an efficient adaptive fault diagnosis algorithm to diagnose an H S D C m ( m ) within three test rounds, and at most N + 4 m ( m − 2 ) tests with m ≥ 3 (resp. at most nine tests with m = 2 ), where N = m · 2 m is the total number of nodes in H S D C m ( m ) . Our experimental outcomes demonstrate that this diagnosis scheme of HSDC can achieve complete diagnosis and significantly reduce the number of required tests.

Keywords: data center networks; diagnosability; adaptive diagnosis; hamiltonian; cycle decomposition (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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