Efficient Parallel Adaptive Diagnosis of a Class of Data Center Networks

Designing a data center network (DCN) architecture capable of accommodating and managing thousands of servers is crucial for optimizing computing services in fields, such as Big Data, cloud computing, and artificial intelligence. This article introduces a novel class of DCN architecture, termed hypercube-like data center network (HLDN), which extends existing frameworks like high scalability data center network and crossed cube-based scalability data center network, while exploring previously unexamined designs. As the scale of DCNs grows and the number of servers increases, the incidence of network failures also rises, impacting network reliability. To address this challenge, we propose the first adaptive diagnostic schemes specifically designed for DCNs. We investigate the Hamiltonian properties of HLDN and, based on this analysis, present parallel adaptive diagnostic algorithms under the Preparata, Metze, and Chien (PMC) and comparison (COM) models. Simulation experiments validate the effectiveness of these algorithms, showing that the PMC model-based approach significantly outperforms the COM model in terms of diagnostic performance under identical conditions. Furthermore, comparative analyses with existing methods highlight the enhanced diagnostic accuracy of our proposed algorithms. This work not only provides an innovative solution for improving the reliability of DCNs, but also offers valuable theoretical and practical insights for fault detection and management in large-scale network systems.