It Is Time to Address Network Power Proportionality
Abstract
In recent years, networking hardware development has primarily focused on speed rather than power efficiency. By contrast, computing hardware has received a lot more attention given its dominant power footprint, especially in machine-learning (ML) data centers. With faster networks, we spend less time communicating and get more useful work out of the (increasingly expensive) computing hardware. But, the faster the network, the more time it idles and the worse its energy efficiency, which is magnified by the notorious lack of power proportionality of networking equipment. In this paper, we analyze the network power footprint in a production ML cluster and find that it accounts for a still sizeable fraction of the total (12%) and that, by improving network power proportionality to match that of the compute, one could save close to 9% of the overall cluster energy demand. We argue that this potential is worth investigating and discuss opportunities and challenges to address power proportionality in networking hardware, which we invite the networking research community to tackle.
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BibTex
@inproceedings{rollin2025powerproportionality,
title = {It Is Time to Address Network Power Proportionality},
booktitle = {24th {{ACM Workshop}} on {{Hot Topics}} in {{Networks}} ({{HotNets}} 2025)},
author = {R{"o}llin, Lukas and Jacob, Romain and Vanbever, Laurent},
year = {2025},
eprint = {20.500.11850/784030},
eprinttype = {hdl},
url = {http://hdl.handle.net/20.500.11850/784030},
urldate = {2025-09-22},
abstract = {In recent years, networking hardware development has primarily focused on speed rather than power efficiency. By contrast, computing hardware has received a lot more attention given its dominant power footprint, especially in machine-learning (ML) data centers. With faster networks, we spend less time communicating and get more useful work out of the (increasingly expensive) computing hardware. But, the faster the network, the more time it idles and the worse its energy efficiency, which is magnified by the notorious lack of power proportionality of networking equipment. In this paper, we analyze the network power footprint in a production ML cluster and find that it accounts for a still sizeable fraction of the total (12%) and that, by improving network power proportionality to match that of the compute, one could save close to 9% of the overall cluster energy demand. We argue that this potential is worth investigating and discuss opportunities and challenges to address power proportionality in networking hardware, which we invite the networking research community to tackle.},
}Research Collection: 20.500.11850/784030
Slide Sources: https://gitlab.ethz.ch/projects/60440