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Xuanli Liu

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2026

A Scalable and High-Performance Architecture for Data Center Networks

The rapid expansion of cloud computing, big data analytics, and artificial intelligence has positioned data centers as the backbone infrastructure of modern computing. Data center networks (DCNs) play a critical role in determining overall system performance and reliability. Existing DCN architectures face limitations, such as difficulties in balancing throughput and latency, insufficient fault-tolerance, and high expansion costs. To address these challenges, we propose ACDC (Augmented Cube-based Data Center), a novel server-centric DCN topology based on augmented cubes that achieves superior performance while maintaining cost-effectiveness through exclusive use of dual-port servers and low-port commodity switches. Firstly, we analyze the key features and properties of ACDC, with a focus on scalability and network diameter, establishing rigorous theoretical foundations for the proposed architecture. Secondly, we present comprehensive routing algorithms including ARouting for fault-free scenarios and AFR for fault-tolerant communication. Finally, extensive experimental evaluations demonstrate ACDC’s superior performance compared to state-of-the-art DCN architectures. Experimental results show that ACDC achieves a network diameter approximately 75% smaller than HSDC and 50% smaller than AQDN. Furthermore, ACDC maintains comparable throughput to the Fat-Tree under random traffic scenarios, while demonstrating substantial advantages under high-density all-to-all communication patterns, achieving at least 69.1% improvement in average throughput and at least 40.7% reduction in flow completion time compared to AQDN, DCell and FiConn. These confirm that ACDC strikes a good balance among performance, cost-efficiency, scalability, and fault-tolerance in contrast to the state-of-the-art DCN architectures.

Xuanli Liu, Weibei Fan, Zhenjiang Dong et al. · 0 citations