> The results were striking: compared to traditional fat-tree networks, RNG (Resilient Network Graphs) uses 69% fewer routers, delivers 33% higher throughput, cuts network power by 40%, and lowers operating costs by 27. In early 2026, RNG became the default design for most newly built Amazon data centers globally.
> For cabling, they developed the ShuffleBox—a passive optical device whose internal wiring combined with randomized ShuffleBox-to-ShuffleBox cabling yields “quasi-random” graphs that behave like truly random graphs.
This is pretty incredible, random layouts of networks that have on-average better properties...
I'm really curious about the long tail of performance though. What is the worst case scenario here? And are there some better case scenarios? Uniformity in Clos networks is pretty great, but many loads don't need uniformity, and if these RNG-based networks have non-uniformity, perhaps that has operational characteristics that can be helpful or harmful.
"Performance guarantees are stochastic rather than deterministic. The worst case performance (for metrics such as number of hops and oversubscription) is known, but for RNG our models are stochastic (i.e., the worst case performance is known with high probability). This is a weaker limitation than it might appear. Fat-tree guarantees are also effectively stochastic once you account for real-world failures, which are frequent at scale. RNG simply makes the stochastic nature explicit and designs for it from the start."
Well I guess I'd like to see those guarantees, but more specifically, the variance of them.
I think Section 9, and Figures 13/14 in the Arxiv preprint sort of address this, but it doesn't mention anything about accounting for real-world failures in fat trees. I haven't had a chance to read it all, though...
Interesting reading this because this is essentially the principle behind https://socketcluster.io/ scalability; the sharding of channels across available brokers is pseudo-random. It uses a hash function for determinism but the distribution appears to be random and that was also the best way I could find to distribute load evenly between available nodes. It is key to its embarrassingly parallel design.
It's interesting to see it being done at the data centre level as well.
That's a different thing entirely, that assumes you already have a physical layer that allows any client to connect to any broker, this is about building that physical layer
Although very interesting from a scientific point of view I always consider these use cases to be very unique to AWS / hyperscaler DC's and in no way a new 'blueprint' for how to build DC-networks/fabrics.
Edit: Answering my own question, Jellyfish proved theoretically that random networks can be better, and this is a working implementation based on that solves the problems with creating/operating random networks.
I heard from several engineers several years ago that egress was metered because their network did not have enough switching capacity to make it truly unmetered at AWS scale. Looks like this might have been a part of the solution.
I recognize this blog from the layout / design. It was impressive the previous time I say it. This is pretty far beyond my level of comprehension, but I get the gist of it and it's pretty interesting. I don't understand why this works better, though. It's sort of, "the math makes it this way" and I don't know why. I do understand how it supersedes trees, so that's pretty cool for future networking.
It's not that dissimilar to how the Internet works. Although you have some steering like IX peering switches, and social/economic factors, but in whole it is fairly random.
I get the feeling I am missing some info (Like what is meant here by Data Center Networks) thats preventing me from understanding whats happening here. I am guessing that this falls outside of the traditional rack/colo paradigm and has more to do with hyperscalers.
AWS hasn't published all that much about their network, but we know they use a high-radix folded Clos fabric because they mention it in their paper about Scalable Reliable Datagram. If you want an overview of the folded Clos fabric, try reading Google's "Jupiter Rising" paper. https://dl.acm.org/doi/pdf/10.1145/2829988.2787508
Then, if you want to know about using optical switches to connect Clos segments without a fixed spine, check out Google's "Mission Apollo" paper: https://arxiv.org/pdf/2208.10041
It's much larger but fundamentally it's not that different. In each rack you have one or two switches. How do you connect those racks to each other? The standard answer (simplified) is centralized spine switches but AWS discovered that a random network where the rack switches connect directly to each other is cheaper.
I am moderately insulted that they want me to believe that graph theoreticians at Amazon sit at a desk with a bunch of optical T&M equipment. Sort of silly!
I am fairly confident that is a typo. The paper says "Our analysis reveals that RNG topologies are 9–45% cheaper than fat trees with equivalent oversubscription ratio." The blogger probably left off the % sign.
Oh man, James Hamilton blog posts, I love these things! (Edit: for more concrete details, the Arxiv paper linked from the blog post is here https://arxiv.org/pdf/2604.15261 and the amazon.science link has some higher level view of the details https://www.amazon.science/blog/how-flat-is-replacing-fat-in... )
> The results were striking: compared to traditional fat-tree networks, RNG (Resilient Network Graphs) uses 69% fewer routers, delivers 33% higher throughput, cuts network power by 40%, and lowers operating costs by 27. In early 2026, RNG became the default design for most newly built Amazon data centers globally.
> For cabling, they developed the ShuffleBox—a passive optical device whose internal wiring combined with randomized ShuffleBox-to-ShuffleBox cabling yields “quasi-random” graphs that behave like truly random graphs.
This is pretty incredible, random layouts of networks that have on-average better properties...
I'm really curious about the long tail of performance though. What is the worst case scenario here? And are there some better case scenarios? Uniformity in Clos networks is pretty great, but many loads don't need uniformity, and if these RNG-based networks have non-uniformity, perhaps that has operational characteristics that can be helpful or harmful.
"Performance guarantees are stochastic rather than deterministic. The worst case performance (for metrics such as number of hops and oversubscription) is known, but for RNG our models are stochastic (i.e., the worst case performance is known with high probability). This is a weaker limitation than it might appear. Fat-tree guarantees are also effectively stochastic once you account for real-world failures, which are frequent at scale. RNG simply makes the stochastic nature explicit and designs for it from the start."
Well I guess I'd like to see those guarantees, but more specifically, the variance of them.
I think Section 9, and Figures 13/14 in the Arxiv preprint sort of address this, but it doesn't mention anything about accounting for real-world failures in fat trees. I haven't had a chance to read it all, though...
Interesting reading this because this is essentially the principle behind https://socketcluster.io/ scalability; the sharding of channels across available brokers is pseudo-random. It uses a hash function for determinism but the distribution appears to be random and that was also the best way I could find to distribute load evenly between available nodes. It is key to its embarrassingly parallel design.
It's interesting to see it being done at the data centre level as well.
That's a different thing entirely, that assumes you already have a physical layer that allows any client to connect to any broker, this is about building that physical layer
There's some important nuances stated by Ivan Pepelnjak who is considered an authority in networking: https://blog.ipspace.net/2026/06/goodbye-leaf-spine-networks...
Although very interesting from a scientific point of view I always consider these use cases to be very unique to AWS / hyperscaler DC's and in no way a new 'blueprint' for how to build DC-networks/fabrics.
How is this different from Jellyfish? I recall reading a paper around that back in the 2010s https://www.usenix.org/system/files/conference/nsdi12/nsdi12...
Edit: Answering my own question, Jellyfish proved theoretically that random networks can be better, and this is a working implementation based on that solves the problems with creating/operating random networks.
One interesting consequence of this is that it's now (for the first time!) possible to get unlimited AWS egress.
It's not cheap, and it's limited to `us-east-1`, but it's at least _possible_ now via AWS Interconnect: https://aws.amazon.com/interconnect/lastmile/pricing/
It's not a consequence of this.
And that's their... third... fourth? completely separate system for connecting an AWS virtual network to a physical DC in the outside world?
Yes, there are several other options. This is the first one that is not metered.
Might not be GB metered, but it is metered by the port-size/Hour at a very heavily marked up rate.
I don't think these things have anything to do with each other.
I heard from several engineers several years ago that egress was metered because their network did not have enough switching capacity to make it truly unmetered at AWS scale. Looks like this might have been a part of the solution.
I recognize this blog from the layout / design. It was impressive the previous time I say it. This is pretty far beyond my level of comprehension, but I get the gist of it and it's pretty interesting. I don't understand why this works better, though. It's sort of, "the math makes it this way" and I don't know why. I do understand how it supersedes trees, so that's pretty cool for future networking.
It's not that dissimilar to how the Internet works. Although you have some steering like IX peering switches, and social/economic factors, but in whole it is fairly random.
On the internet each ISP is looking for congested links and expanding those links, like a lazy-loaded fat tree.
I get the feeling I am missing some info (Like what is meant here by Data Center Networks) thats preventing me from understanding whats happening here. I am guessing that this falls outside of the traditional rack/colo paradigm and has more to do with hyperscalers.
AWS hasn't published all that much about their network, but we know they use a high-radix folded Clos fabric because they mention it in their paper about Scalable Reliable Datagram. If you want an overview of the folded Clos fabric, try reading Google's "Jupiter Rising" paper. https://dl.acm.org/doi/pdf/10.1145/2829988.2787508
Then, if you want to know about using optical switches to connect Clos segments without a fixed spine, check out Google's "Mission Apollo" paper: https://arxiv.org/pdf/2208.10041
It's much larger but fundamentally it's not that different. In each rack you have one or two switches. How do you connect those racks to each other? The standard answer (simplified) is centralized spine switches but AWS discovered that a random network where the rack switches connect directly to each other is cheaper.
Good 6 minute video explainer: https://youtube.com/watch?v=yDoRYRRPOA0
I am moderately insulted that they want me to believe that graph theoreticians at Amazon sit at a desk with a bunch of optical T&M equipment. Sort of silly!
The win in operating costs is impressive, bordering in the unbelievable (27x). Does anyone have a clue about where the win comes from?
It's almost certainly meant to be 27%, not a factor of 27.
I am fairly confident that is a typo. The paper says "Our analysis reveals that RNG topologies are 9–45% cheaper than fat trees with equivalent oversubscription ratio." The blogger probably left off the % sign.
I always like these randomized/semi-randomized network papers. Here's a little known one you might enjoy if you liked this one. https://repositorio.unican.es/xmlui/handle/10902/23594
[flagged]