🧱 Networking Planes: The Foundation of Distribution #
Modern network devices are logically divided into three planes:
- Management Plane — configuration, monitoring, and lifecycle operations
- Control Plane — topology discovery, routing computation, and policy decisions
- Forwarding Plane (Data Plane) — real-time packet processing and transmission
As networking evolved from simple packet delivery to AI-scale data movement, the forwarding plane underwent the most dramatic transformation—shifting from centralized to deeply distributed architectures.
🔄 Centralized vs. Distributed Forwarding Models #
At the heart of any router are two core data structures:
- RIB (Routing Information Base) — built by protocols such as BGP or OSPF
- FIB (Forwarding Information Base) — a hardware-friendly projection of the RIB used for packet forwarding
Centralized Forwarding #
In early router designs:
- A single FIB resides on the main control board
- Line cards forward packets by querying the central processor
- Throughput scales poorly as all traffic converges on one bottleneck
This model quickly collapses under high bandwidth and low-latency demands.
Distributed Forwarding #
Modern high-performance routers instead:
- Replicate the FIB across all line cards
- Allow each card to independently forward packets
- Eliminate the central forwarding bottleneck
This architectural shift is the first major step toward scalability.
🧩 Cell-Based Distributed Forwarding: The Broadcom DNX Model #
As port speeds increased, even distributed packet forwarding became insufficient. The next evolution was cell-based internal switching, exemplified by the Broadcom DNX / Jericho family.
Internal Pipeline #
- Ingress Processing
- Packet parsing
- Lookup and classification
- Cellization
- Variable-length Ethernet frames sliced into fixed-size cells
- Switch Fabric Transit
- Cells traverse the fabric via Ramon fabric chips
- Egress Processing
- Cells reassembled
- Headers rewritten (MAC, VLAN, MPLS, etc.)
This design decouples external packet formats from internal transport, enabling extreme bandwidth scaling.
Credit-Based Flow Control #
To prevent fabric congestion:
- Egress cards advertise available buffer credits
- Ingress cards must request permission before sending cells
- If the destination is congested, traffic is buffered upstream
This lossless, backpressure-driven model becomes critical for AI workloads later.
🧠Distributed Control Planes and Data Center Networking #
The same distribution principles expanded beyond routers into entire data centers.
SDN and Control Separation #
- OpenFlow / SDN centralized the control plane on x86 servers
- Physical switches retained fast, distributed forwarding logic
- Enabled global policy with local execution
IP Clos (Spine–Leaf) Topologies #
Modern Ethernet data centers rely on Clos fabrics:
- Leaf switches connect to servers
- Spine switches provide non-blocking interconnect
- Routing decisions are decentralized
If a link or spine fails, local rerouting happens immediately, ensuring resilience without centralized intervention.
🧠AI Data Centers: The Network as a Supernode #
AI workloads demand lossless, deterministic Ethernet to keep GPUs and XPUs fully utilized. Architects generally face two approaches:
- Single-Chassis Systems
- Tight control
- Limited scale
- Spine–Leaf IP Clos
- Virtually unlimited scale
- Requires advanced congestion control (ECN, PFC, DCQCN)
Jericho3-AI: Router Principles at Data Center Scale #
Broadcom’s Jericho3-AI extends modular router design across the entire fabric:
- Leaf switches: Jericho-class chips
- Spine fabric: Ramon chips
- The data center behaves like a giant distributed chassis
From the GPU’s perspective, thousands of switches collapse into a single logical forwarding plane—a true network-level supernode.
🚀 The Next Frontier: Ultra Ethernet (UE) #
Looking beyond 2026, Ultra Ethernet (UE) targets clusters with up to 1 million XPUs. The key innovation is extending scheduling and reliability from the network core all the way to endpoints.
Core enabling technologies include:
- NSCC / RCCC
- Fine-grained, end-to-end congestion control
- LLR (Link-Level Retry)
- Reliability enforced at the physical link layer
- CBFC (Credit-Based Flow Control)
- Cell-level backpressure extended across the full fabric
The result is a network where loss avoidance, not loss recovery, is the default behavior.
🧠Conclusion: Distribution as the Only Scalable Path #
From classic routers to AI supernodes, one lesson repeats:
Centralized architectures inevitably hit performance walls.
By distributing:
- forwarding logic
- control intelligence
- congestion management
modern networks escape those limits. AI data centers are not a break from networking history—they are its logical conclusion: the entire fabric operating as one massively distributed computer.