Hyper‑Converged Data Center Network Architecture and Its Impact on Computational Efficiency

The article introduces the significance of data‑center networking for computational power, identifies three resource zones—storage, high‑performance computing (HPC), and general‑purpose computing—and describes how the network acts as the central nervous system linking these zones throughout the data‑center lifecycle.

Computational Efficiency (CE) is defined as the ratio of data‑center compute output to total IT power consumption (FLOPS/W). Enhancing network capabilities can markedly improve CE; for example, Ethernet‑based compute‑and‑network (ODCC2019) reduces HPC task completion time by over 20% compared with traditional RoCE, delivering the same compute with 20% lower energy.

In storage networks, NVMe‑over‑Fabric (NVMe‑of) lossless Ethernet can boost IOPS by up to 87% versus traditional Fibre Channel, reducing end‑to‑end latency and supporting greener data‑centers.

The shift from HDD to SSD has driven a near‑100× performance increase, but FC networks become bottlenecks; NVMe‑of RoCE offers superior ecosystem scale, openness, and SDN‑based management, positioning it as the next‑generation storage network.

PCIe bandwidth limits for CPU/GPU interconnects are highlighted, with AI‑accelerator chips (e.g., Habana Gaudi, Huawei Ascend 910) integrating RoCE Ethernet ports directly on the die to bypass PCIe bottlenecks and enable scalable, high‑throughput AI training.

IPv6 deployment is emphasized as essential for the massive address demand of future data‑center interconnections.

The next‑generation hyper‑converged network should feature: (1) fully lossless Ethernet supporting unified traffic for compute, storage, and HPC; (2) lifecycle‑wide automation via digital‑twin models, big‑data, and AI for planning, deployment, maintenance, and optimization; (3) service‑oriented architecture exposing physical, logical, application, interconnect, security, and analytics services for multi‑cloud and industry scenarios.

Best practices include AI‑driven dynamic queue management to replace static watermarks, millisecond‑level fault detection with coordinated storage failover, and plug‑and‑play storage provisioning over Ethernet, dramatically reducing configuration complexity.

Performance challenges such as dynamic and static latency, hop count, and ingress frequency are discussed, with modern lossless Ethernet reducing these factors to meet HPC requirements.

Traditional Ethernet switches incur 600 ns–1 µs static forwarding latency; newer designs aim to lower this further. CLOS architectures, while flexible, sacrifice latency and cost‑effectiveness, prompting research into alternative topologies.