How flat is replacing fat in AWS data center networks

How flat is replacing fat in AWS data center networks - Amazon Science

Close

Close

Social

bluesky

threads

twitter

instagram

youtube

facebook

linkedin

github

rss

Menu

Research

Research areas

Automated reasoning

Cloud and systems

Computer vision

Conversational AI

Economics

Information and knowledge management

Machine learning

Operations research and optimization

Quantum technologies

Robotics

Search and information retrieval

Security, privacy, and abuse prevention

Sustainability

Our scientific contributions

Publications

Research from our scientists and collaborators.

Conferences

Our experts present and discuss cutting-edge research at scientific meetings globally.

Research areas

Automated reasoning

Cloud and systems

Computer vision

Conversational AI

Economics

Information and knowledge management

Machine learning

Operations research and optimization

Quantum technologies

Robotics

Search and information retrieval

Security, privacy, and abuse prevention

Sustainability

Our scientific contributions

Publications

Research from our scientists and collaborators.

Conferences

Our experts present and discuss cutting-edge research at scientific meetings globally.

News & blog

The latest from Amazon researchers

Amazon Science Blog

Technical deep-dives and perspectives from our scientists.

News

Research milestones and recent achievements.

The latest from Amazon researchers

Amazon Science Blog

Technical deep-dives and perspectives from our scientists.

News

Research milestones and recent achievements.

Collaborations

Amazon Research Awards

Overview

Call for proposals

Latest news

Research stories

Recipients

Amazon Nova AI Challenge

Overview

Rules

FAQs

Teams

Research collaborations

Overview

Carnegie Mellon University

Columbia University

Hampton University

Howard University

IIT Bombay

Johns Hopkins University

Max Planck Society

MIT

Tennessee State University

University of California, Los Angeles

University of Illinois Urbana-Champaign

University of Southern California

University of Texas at Austin

Virginia Tech

University of Washington

Amazon Research Awards

Overview

Call for proposals

Latest news

Research stories

Recipients

Amazon Nova AI Challenge

Overview

Rules

FAQs

Teams

Research collaborations

Overview

Carnegie Mellon University

Columbia University

Hampton University

Howard University

IIT Bombay

Johns Hopkins University

Max Planck Society

MIT

Tennessee State University

University of California, Los Angeles

University of Illinois Urbana-Champaign

University of Southern California

University of Texas at Austin

Virginia Tech

University of Washington

Resources

Code and datasets

AGI Labs

Meet the team building useful AI agents.

Amazon Nova

Try Amazon’s frontier foundation models.

Code and datasets

AGI Labs

Meet the team building useful AI agents.

Amazon Nova

Try Amazon’s frontier foundation models.

Careers

Careers

Explore our open roles.

Amazon Scholars

Faculty research opportunities on industry-scale technical challenges.

Postdoctoral Science Program

Early-career research opportunities alongside experienced industry scientists.

Careers

Explore our open roles.

Amazon Scholars

Faculty research opportunities on industry-scale technical challenges.

Postdoctoral Science Program

Early-career research opportunities alongside experienced industry scientists.

Search

Submit Search

Cloud and systems

How flat is replacing fat in AWS data center networks

“Quasi-random” network topologies and new passive optical components called ShuffleBoxes make more-efficient flat networks as practical as traditional “fat-tree” networks.

By Giacomo Bernardi , Ratul Mahajan , Seshadhri Comandur

May 28, 2026

6 min read

Share

Share

Copy link

Email

X

LinkedIn

Facebook

Line

Reddit

QZone

Sina Weibo

WeChat

WhatsApp

分享到微信

x

Routing in today’s data centers is usually governed by a data structure called a “fat tree”, which is similar to a corporate organizational chart, with nodes in each layer connecting to multiple nodes in the layer below. Here, however, the nodes of the bottom layer represent routers that want to send messages to each other, and the layers above them contain extra routers that simplify the routing procedure. A message sent by one bottom-layer router climbs the tree until it reaches the branch that leads to the destination router, and then it is sent down.

A fat tree. Each node represents a router, and each router has four ports. Nodes T1 – T12 reserve two ports each for connecting to servers.

This design is easy to implement but inefficient: the extra layers of routers add overhead, and routers at the top of the tree are prone to congestion. The fat-tree structure is also fragile, since the loss of a single router can cut off large regions of the tree. Theoretically, the best alternative is a “flat” network, in which the routers connect directly to each other. Ideally, one should connect the routers randomly, to maximize the diversity of routes through the network. But this is impractical, because calculating ad hoc paths through a random network is computationally intensive, and randomly connecting routers leads to data centers criss-crossed with wires.

Twelve routers (T1 – T12) linked through a fat tree (left) and a flat network (right) . Each router has four ports; routers T1 – T12 reserve two ports each for connections to servers.

In a paper we recently posted to arXiv , we describe the first ever scalable flat-network datacenter. We introduce a “quasi-random” network topology that preserves many of the benefits of random connection and a passive optical component we call a ShuffleBox, which makes it practical to cable a flat network. The resulting network design — which we call RNG, for resilient network graphs — is now used in AWS data centers and is the default for most new builds globally. It uses 69% fewer routers, delivers up to 33% better throughput, and projects a 40% reduction in network equipment electricity consumption.

The secret of randomness

In the early 1990s, mathematicians showed that the optimal network for routing has a random topology, in which each router simply connects randomly to a few others. This is quite counterintuitive, but the overall network ends up having lots of different paths between all pairs of routers. Random networks also demonstrate excellent resilience, since no single router is more important than any…