AI training now floods datacenter fabrics with thousands of simultaneous collectives, yet most frameworks still move data the hard way: O(N) unicasts for a group of N processors. Classic multicast could slash those bytes but has long been deemed unscalable: computing an optimal tree in an asymmetric Clos is NP-hard and group-specific rules quickly exhaust switch TCAM.

Cloud computing and AI workloads are driving unprecedented demand for efficient communication within and across datacenters. However, the coexistence of intra- and inter-datacenter traffic within datacenters plus the disparity between the RTTs of intra- and inter-datacenter networks complicates congestion management and traffic routing. Particularly, faster congestion responses of intra-datacenter traffic causes rate unfairness when competing with slower inter-datacenter flows. Additionally, inter-datacenter messages suffer from slow loss recovery and, thus, require reliability. Existing solutions overlook these challenges and handle inter- and intra-datacenter congestion with separate control loops or at different granularities. We propose Uno, a unified system for both inter- and intra-DC environments that integrates a transport protocol for rapid congestion reaction and fair rate control with a load balancing scheme that combines erasure coding and adaptive routing. Our findings show that Uno significantly improves the completion times of both inter- and intra-DC flows compared to state-of-the-art methods such as Gemini.

Microsecond-scale congestion events, known as microbursts, are a main cause of packet loss and poor application performance in today's datacenters. Given the low network utilization in datacenters, one would expect packet deflection, in-situ re-routing of packets that arrive at a full buffer to a different port, to effectively prevent packet loss. However, if deployed naively, deflection leads to excessive packet re-ordering, exacerbated congestion, and head-of-the-line blocking in switch buffers. In this study, we resolve the above challenges by selectively deflecting the packets that cause persistent congestion in the network. To enable this, we augment the end-host network stacks with a transport-independent extension that tracks and marks flows with their remaining bytes. Our in-network deflection component uses the flow size information to re-route packets from flows with more data to send. Finally, an extension to the receive-side of end-host stacks retrieves the correct ordering of packets before passing them to transport and higherlevel protocols. We evaluate our design, Vertigo, under diverse datacenter workloads and show that it is effective in managing microbursts under light and heavy loads and when combined with various congestion control algorithms. For example, in a leaf-spine network under 85% load, Vertigo reduces the mean incast query completion times by 3.5x, 3.3x, 5x compared to ECMP, DRILL, and DIBS when using TCP, 3x, 3.5x, 4.5x alongside DCTCP, and 43x, 33x, 16x when using Swift, respectively.