EVPN Active-Active Multihoming in Data Center Fabrics

One of the most interesting capabilities introduced by EVPN is native active-active multihoming. If you’ve spent time designing data center networks over the last decade, you’ve most likely used or at least encountered technologies like vPC, MLAG, MC-LAG, Virtual Chassis, StackWise Virtual, or other similar vendor-specific ways to dual-home servers and access switches.

These technologies solved a real problem. We wanted to connect a device to multiple switches simultaneously while still allowing all available links to forward traffic. Traditional Layer 2 protocols such as STP prevented loops, but they also left redundant links sitting idle which is a big waste of resources. 

The challenge is that most MLAG implementations are proprietary. They use special peer links and synchronization mechanisms, and usually that means you’ll be locked into a particular vendor architecture.

EVPN Active-Active Multihoming (EVPN-MH) takes a different approach. Instead of relying on proprietary control planes between switches, EVPN uses BGP EVPN itself to advertise multihoming information throughout the fabric. The result is a standards-based solution that provides redundancy, load balancing, faster convergence, and better scalability for modern VXLAN fabrics.

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The Five EVPN Route Types in VXLAN Data Center Fabrics

Many modern enterprise and cloud data center networks have standardized on a combination of BGP, EVPN, and VXLAN to build scalable Layer 2 and Layer 3 fabrics. Whether the environment is only a few dozen switches or thousands of leaf and spine devices, EVPN has become the preferred control plane for distributing endpoint reachability information across the fabric.

An important concept to understand when first learning EVPN are the route types used within the EVPN address family. Unlike traditional BGP, which primarily exchanges IP prefixes, EVPN distributes a variety of information. Each route type serves a specific purpose and helps the fabric build a complete picture of where endpoints, gateways, and services exist.

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Introduction to Collective Communications in AI Data Center Networking

For decades, network architects designed data center networks primarily around application and storage traffic. Whether supporting enterprise applications, web services, virtualization platforms, or cloud-native workloads, the network was the transport mechanism connecting users, applications, and data.

Traffic patterns were characterized by north-south communication flows, predictable east-west application exchanges, and a relatively loose coupling between compute and network performance. High-performance computing environments differed somewhat by emphasizing ultra-high bandwidth, low latency, and efficient inter-node communication for distributed workloads. However, modern AI infrastructure introduces a fundamentally different model.

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