MRLS leaf-spine networks deliver 50% higher throughput than Fat-Tree and 100% higher than Dragonfly for All2All collectives with 100k endpoints via simulation evaluation.
RNG: Flat Datacenter Networks at Scale
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abstract
We design and deploy in production the first flat datacenter networks. Our design, called RNG, is based on quasi-random graphs. While the cost and fault-tolerance benefits of such topologies have been long known, their practical realization has been hampered by a lack of scalable routing and cabling approaches. RNG has a new distributed routing protocol that exploits the properties of random graphs to find a large number of edge disjoint paths between pairs of endpoints. It uses a novel passive optical device that internally shuffles cables, which makes its cabling complexity similar to that of fat trees. We show that RNG matches or exceeds the performance of fat trees for a range of traffic patterns, despite being up to 45% cheaper. RNG is now the default datacenter network for most workloads at Amazon.
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cs.NI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Extreme-Scale Interconnection Networks
MRLS leaf-spine networks deliver 50% higher throughput than Fat-Tree and 100% higher than Dragonfly for All2All collectives with 100k endpoints via simulation evaluation.