AWS 用 RNG 解决了胖树网络的流量瓶颈，做 AI 训练和大规模云计算的团队可以直接受益——更少的硬件、更低的成本、更好的吞吐，值得点开了解工程细节。
Amazon 发布了新型数据中心网络架构“Resilient Network Graphs”（RNG），用扁平随机图替代传统的胖树结构，将硬件需求降低 69%，吞吐量提升 33%。该设计自去年起已在 AWS 数据中心悄然部署，现已成为大多数 AWS 工作负载的默认网络。RNG 通过扁平化随机图连接路由器，提供多条独立路径，避免胖树网络的流量瓶颈问题。其路由系统 Spraypoint 将流量分散到多条路径，ShuffleBox 布线设备使随机连接变得可实施。实测显示，RNG 在性能上与胖树网络持平，但成本降低 9% 至 45%，尤其对 AI 集群的大规模同步训练流量有显著改善。
Amazon unveiled “Resilient Network Graphs,” (RNG) …
Amazon unveiled “Resilient Network Graphs,” (RNG) a data center network that reduces hardware needs by 69% and raises throughput by 33%. It is now default for most AWS workloads.
They revealed that it has been quietly deploying the design across its data centers since last year, and it is now the default data center network for most AWS workloads.
It replaced tree-shaped datacenter networks with flatter random ones that waste less capacity.
For decades, fat-tree networks worked because they were predictable, but their layered shape can concentrate traffic at choke points while other links sit underused. So the problem is that fat-tree networks are easy to run, but their hierarchy can trap traffic on a few links while other links sit unused.
“Resilient Network Graphs,” (RNG) fixes this by connecting routers in a flat quasi-random graph, so many different paths exist between servers instead of a few fixed routes through upper layers.
RNG attacks the problem by flattening the fabric into a quasi-random graph, where many small independent paths replace a few privileged routes.
Its routing system, Spraypoint, spreads traffic across many separate paths, while its ShuffleBox cabling device makes the random-looking wiring practical to build and expand.
Instead of asking every packet to chase the shortest path, Spraypoint fans traffic outward and then guides it back through distributed waypoints, creating many edge-disjoint paths without requiring exotic switch memory.
The authors tested RNG in 2 real Amazon production fabrics and compared it with fat-tree networks using transport and storage workloads.
The main result is that RNG matched fat-tree application performance, found far more separate paths than common routing methods, and was estimated to cost 9% to 45% less.
The hard part is not the idea, but the engineering, because routing in a random mesh needs smarter path selection and the physical system must manage millions of fiber connections without becoming impossible to operate.
This is important for AI clusters because training traffic is huge, synchronized, and sensitive to congestion, so a network that spreads load better can make expensive GPUs spend less time waiting.
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Link – arxiv. org/abs/2604.15261
Title: "RNG: Flat Datacenter Networks at Scale"