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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...…
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... Continue Reading
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…
Over the past decade, Clos-based leaf-spine architectures have become the default design for data center networking. They deliver predictable latency, horizontal scalability, and clean integration with BGP EVPN/VXLAN overlays. For most enterprise and cloud workloads, they re still the right architecture. But large-scale AI training, particularly distributed training of large language models, has…
Leaf-spine is the default. But it’s not always the right answer. In modern data center networking, the leaf-spine Clos fabric has become the default architecture for good reason. It’s predictable, scalable, and aligns well with familiar Ethernet-based designs and operational models. But as workloads evolve, particularly with the rise of large-scale AI and distributed systems,... Continue Reading
The fastest GPU is only as fast as the slowest packet. For years, data center design has been driven by compute density, virtualization efficiency, and east-west traffic patterns dominated by many small flows. That model breaks down entirely when you step into AI training environments. What you’re building now isn’t just a traditional data center.... Continue Reading
Over the last year, we’ve seen explosive interest in agentic AI systems built with LLM-powered components that can reason, plan, call tools, and coordinate with other agents. But if you’ve actually tried to build a multi-agent workflow, whether for network automation, observability, or network incident response, you’ve probably run into one glaring problem all... Continue Reading
If you work in technology long enough, every new framework starts to sound like “yet another API layer.” The Model Context Protocol (MCP) is different. It’s not a product, and it’s not tied to any one vendor or model. It’s an open standard for how AI apps talk to tools and data providing a standardized,... Continue Reading
Cisco’s latest announcement at Partner Summit 2025 introduced Cisco Unified Edge, a converged compute platform designed to bring agentic and inferencing AI workloads closer to where data is actually generated, such as the branch, the factory, the retail floor, or the hospital wing. The idea is that instead of sending massive data streams back and... Continue Reading
I ve been speaking with more folks again recently about AI initiatives in their IT organizations. Most recently it was a systems team that ran the on-prem server infrastructure, Azure environment, and just recently also tasked with end-user computing. What struck me in the conversation was how the discussion kept going back to AI. That might... Continue Reading