RNG: Flat Datacenter Networks at Scale
The paper presents RNG, a new design for flat datacenter networks that utilizes quasi-random graphs. RNG features a distributed routing protocol that enhances fault tolerance and reduces costs compared to traditional network topologies. It has been adopted as the default network for most workloads at Amazon, demonstrating superior performance and cost efficiency.
- ▪RNG is based on quasi-random graphs and aims to improve datacenter network design.
- ▪The new distributed routing protocol allows for multiple edge disjoint paths between endpoints.
- ▪RNG is up to 45% cheaper than traditional fat tree networks and matches or exceeds their performance.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2604.15261 |
| Publication time | Thu, 28 May 2026 20:52:54 +0000 |
| Retrieval time | 2026-05-28T20:59:38.054Z |
| Last seen | 2026-05-28T20:59:38.054Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | qq1oJnkbr8Y4 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Networking and Internet Architecture arXiv:2604.15261 (cs) [Submitted on 16 Apr 2026 (v1), last revised 21 May 2026 (this version, v3)] Title:RNG: Flat Datacenter Networks at Scale Authors:Giacomo Bernardi, Ratul Mahajan, C. Seshadhri, Enrico Carlesso, Chinchu Merine Joseph, Saurabh Kumar, Pavan Manikonda, Luiza Popa, Randy Ram, Steven Robinson, Elizabeth Tennent View a PDF of the paper titled RNG: Flat Datacenter Networks at Scale, by Giacomo Bernardi and 10 other authors View PDF HTML (experimental) Abstract:We design and deploy in production the first flat datacenter networks. Our design, called RNG, is based on quasi-random graphs.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.