Low-diameter topologies such as Dragonfly and Slim Fly are increasingly adopted in HPC and datacenter networks, yet existing load balancing techniques either rely on proprietary in-network mechanisms or fail to utilize the full path diversity of these topologies. We introduce Spritz, a flexible sender-based load balancing framework that shifts adaptive topology-aware routing to the endpoints using only standard Ethernet features. We propose two algorithms, Spritz-Scout and Spritz-Spray that, respectively, explore and adaptively cache efficient paths using ECN, packet trimming, and timeout feedback. Through simulation on Dragonfly and Slim Fly topologies with over 1000 endpoints, Spritz outperforms ECMP, UGAL-L, and prior sender-based approaches by up to 1.8x in flow completion time under AI training and datacenter workloads, while offering robust failover with performance improvements of up to 25.4x under link failures, all without additional hardware support. Spritz enables datacenter-scale, commodity Ethernet networks to efficiently leverage low-diameter topologies, offering unified routing and load balancing for the Ultra Ethernet era.

翻译：在HPC和数据中心网络中，诸如蜻蜓拓扑（Dragonfly）和细飞拓扑（Slim Fly）等低直径拓扑正日益普及，然而现有的负载均衡技术要么依赖于专有的网内机制，要么未能充分利用这些拓扑的完整路径多样性。本文介绍Spritz，一种灵活的基于发送端的负载均衡框架，它仅利用标准以太网特性，将自适应的拓扑感知路由功能转移到终端节点上。我们提出了两种算法——Spritz-Scout和Spritz-Spray，它们分别利用ECN、数据包修剪和超时反馈来探索并自适应地缓存高效路径。通过在超过1000个端点的蜻蜓和细飞拓扑上进行仿真，在AI训练和数据中心工作负载下，Spritz在流完成时间上比ECMP、UGAL-L以及先前的基于发送端的方法提升了最高达1.8倍；同时在链路故障下提供稳健的故障切换，性能提升最高达25.4倍，且无需额外的硬件支持。Spritz使得数据中心规模的商用以太网能够高效利用低直径拓扑，为超以太网时代提供了统一的路由和负载均衡解决方案。