In-network aggregation (INA) has emerged as a key technology to alleviate communication bottlenecks in large-scale distributed training, but its performance is often hindered by suboptimal routing. Existing INA-aware routing algorithms suffer from certain limitations: they either lack a global, multi-job coordination mechanism, or operate on incomplete network models that ignore key hardware constraints such as switch processing capacity. These deficiencies lead to network congestion and inefficient resource utilization, ultimately undermining the full potential of INA. To address these challenges, we present INARouting, a novel framework that holistically solves the multi-job hierarchical aggregation routing problem. We propose TINA, a hierarchical aggregation protocol that supports multi-job in-network aggregation. To address different deployment scenarios, we develop two variants: INARouting-Opt that provides optimal solutions for moderate-scale networks, and INARouting-Relax, a fast and effective heuristic using LP-relaxation and a greedy score-based rounding algorithm for large-scale deployments. Through extensive experiments on various scales of Fat-Tree and Spine-Leaf topologies, we demonstrate that INARouting significantly outperforms state-of-the-art methods. INARouting- Opt achieves provably optimal solutions, reducing average job completion time by up to 56% compared to existing methods. Meanwhile, INARouting-Relax outperforms existing algorithms while being 5× faster in solving time, enabling efficient routing in large-scale, dynamic environments.

Recent popular programmable switches provide programmability in packet processing at the line rate, promising to host various Network Functions (NFs). However, the limitations on the switch programmability make it unable to manage runtime dynamic NF states. In the status quo, on-switch NFs rely on the network controller for dynamic state management, suffering from high first-packet latency. This paper proposes DNF, a hardware-software co-design system for dynamic state management of network functions on programmable switches. DNF designs a dynamic State Table on the switch, which providesopportunisticservice to NF flows and sets upfallbackNFs on the server to handle flows that fail on the switch. DNF proposes a state pre-computation andruntime-bindingmethod for per-flow state initialization, which avoids a flow’s runtime interaction with the controller and the consequent latency. DNF devises astate replacementmethod, which periodically swaps hot flows into the switch. It could maximize the switch resource utilization and the throughput. We prototype DNF on P4 switches, implement six representative NFs, and compare DNF with state-of-the-art solutions. Our experiments show DNF reduces the first-packet latency by up to 98% compared with the controller-assisted architecture and improves the system throughput by up to 4.87 times compared with the software solution.