Fault Tolerability Analysis of Data Center Networks Based on h -Component Fault Pattern

With the rapid development of cloud computing, big data, and artificial intelligence, data center networks have become the core of modern computing infrastructure. As an important type of data center network, determining theh-component diagnosability ofk-dimensional DCell networksDCellk,nwith n-port switch has become a critical issue for enhancing network’s diagnostic capabilities and assessing network’s vulnerability. However, there is currently scarce research on the h-component diagnosability ofDCellk,nbased on the mutual testing model. In this paper, we innovatively propose theh-component diagnosability and diagnostic algorithm of DCell networks based on the mutual testing model. We first theoretically prove and determine that the h-component diagnosability of DCellk,n is ctPMC h (DCellk,n) = (h− 1)n + hk− (2h− 2) for 2 ≤h≤ 3 andk≥ (h− 2)n+ (4 −h),n≥h+ 1. It implies that the data center networkDCellk,ncan identify (h− 1)n+hk− (2h− 2) faulty nodes when the count of remaining components is at leasth(2 ≤ h ≤ 3). Furthermore, we also propose a novel and broadly applicableh-componentt-diagnosable algorithm ICFD-P based on iterative testing, combinatorial properties and linearly many fault analysis to diagnose all faulty nodes inDCellk,n, effective as a general framework in interconnection networks. We apply the algorithm ICFD-P to simulated data and real data to diagnose faulty nodes. The simulation experiments highlight the effectiveness and robustness of the designed ICFD-P strategy within and even beyond the allowed range of diagnosability inDCellk,n, combined with its enhanced fault diagnosis capability and theoretical foundation for reliability and security, make it a valuable tool for maintaining the stability and security of such networks.