Fault Diagnosis Based on Subsystem Structures of Data Center Network BCube

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
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Original languageEnglish
Number of pages10
Journal / PublicationIEEE Transactions on Reliability
Online published8 Mar 2022
Publication statusOnline published - 8 Mar 2022


Data center networks (DCNs) always strive to ensure high reliability and fault tolerance when Big Data processing and cloud computing are carried out. One effective method is to conduct the fault diagnosis based on subsystem structures (establish the subsystem-based reliability), which can be transformed into solving the problem of the probability that there exists at least one fault-free subnetwork in one network. In this article, we establish the subsystem-based reliability of BCube, which is the first time that fault diagnosis is carried out in the subsystem structures of the DCN. More specifically, we compute the upper and lower bounds on the subsystem-based reliability of BCube and determine the approximation on the subsystem-based reliability of BCube. Furthermore, we conduct some numerical simulations to validate the established analytical formulation. Our results show that the precise value of subsystem-based reliability of BCube can be basically represented by the approximation value of subsystem-based reliability of BCube, which means that it is much easier to evaluate the reliability of the network even when the network scale is sufficient large. Although the analysis is done for a particular network (BCube), the outcome can serve as a useful reference, and can shed light on the effectiveness of the fault diagnosis for other DCNs.

Research Area(s)

  • Reliability, Servers, Fault diagnosis, Probabilistic logic, Data centers, Telecommunication network reliability, Numerical simulation, BCube, data center network (DCN), fault diagnosis, reliability, subsystem structures, RELIABILITY-ANALYSIS, CONDITIONAL DIAGNOSABILITY, COMBINATORIAL ANALYSIS, PERFORMANCE