Ampel: Scheduling at the Network Cut in ML Training
Abstract
The size and communication patterns of modern ML training workloads place significant strain on datacenter fabrics. When bandwidth demand exceeds capacity, flows experience slowdowns and iteration time grows larger. Fine-grained load-balancing such as packet spraying cannot fully resolve this issue, yet it can shift the bottleneck from individual links to groups of links partitioning the network. In this work, we present Ampel: a system to schedule ML training flows at the network cuts. It leverages packet-spraying’s ability to spread traffic evenly across all available paths to simplify the view of the topology into one only containing potential bottlenecks, and then bridges this new simplified model with past work on coflow scheduling. Our simulated experiments show that Ampel can reduce average training iteration time by up to 18% compared to state-of-the-art ML schedulers.
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BibTex
@inproceedings{torsiello2026ampel,
author = {Torsiello, Valerio and Mishra, Ayush and Das, Sushovan and R{\"{o}}llin, Lukas and Bonato, Tommaso and Hoefler, Torsten and Vanbever, Laurent},
title = {{Ampel: Scheduling at the Network Cut in ML Training}},
booktitle = {SIGCOMM '26: Proceedings of the ACM SIGCOMM 2026 Conference},
address = {Denver, CO, USA},
year = 2026,
month = aug,
publisher = {Association for Computing Machinery},
doi = {10.1145/3789240.3828736},
url = {https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/804300/3789240.3828736.pdf}
}Research Collection: 20.500.11850/804300
Slide Sources: https://gitlab.ethz.ch/projects/65738