netlab release 26.02 is out, including the usual potpourri of goodies:
The fun part, however, are the new container configuration methods:

In the previous posts in this series, we covered the basics of multicast, IGMP, PIM Dense Mode, and PIM Sparse Mode. In the Sparse Mode post, we manually configured the RP address on every router in the network. This works fine in a small lab, but in a larger network with many routers, it becomes difficult to manage. If the RP changes, you have to update the configuration on every single router.

AutoRP solves this problem by allowing routers to dynamically learn the RP address. Instead of manually configuring the RP on each router, you configure one or more routers to announce themselves as Candidate RPs. A separate router (or the same as the Candidate RP router) called the Mapping Agent collects these announcements and distributes the RP information to all other routers in the network. This makes RP management much easier and also provides a way to implement RP redundancy.
There are two methods to dynamically learn the RP address, which are Auto-RP and Bootstrap Router (BSR). In this Continue reading
Brian Linkletter published an updated overview of open-source network simulators and emulators.
containerlab and GNS3 are clear leaders (no surprise there) with the original vrnetlab becoming abandonware (fortunately, we have Roman Dodin’s fork), which makes me think we should focus on using netlab primarily with containerlab and slowly sunset the Vagrant support, particularly considering some people actively hate the license change.
Also, if anyone feels like writing an interface (provider module) between netlab and GNS3, the pull request would be most welcome 😎
Any thoughts? Please leave a comment!
After the enormous speedup I achieved with the FRR containers, I tried to do something similar with the Arista cEOS ones. After all, Arista’s pretty open about running its software on standard Linux, so it should be possible to map host-side configuration files into container-side scripts and execute them, right?
There was just one tiny gotcha: all netlab-generated EOS configuration files are device configuration snippets that are intended to be submitted via EOS CLI, and I didn’t feel like cracking open the netmiko documentation (that’s another backburner project).
However, Arista cEOS includes this magic command called FastCli ;)
Like Google and Meta Platforms, Amazon knows exactly how to infuse AI into its business operations such as online retail, transportation, advertising, and even the Amazon Web Services cloud. …
The Twin Engine Strategy That Propels AWS Is Working Well was written by Timothy Prickett Morgan at The Next Platform.
I have unearthed a few old articles typed during my adolescence, between 1996 and 1998. Unremarkable at the time, these pages now compose, three decades later, the chronicle of a vanished era.1
The word “blog” does not exist yet. Wikipedia remains to come. Google has not been born. AltaVista reigns over searches, while already struggling to embrace the nascent immensity of the web2. To meet someone, you had to agree in advance and prepare your route on paper maps. 🗺️
The web is taking off. The CSS specification has just emerged, HTML tables still serve for page layout. Cookies and advertising banners are making their appearance. Pages are adorned with music and videos, forcing browsers to arm themselves with plugins. Netscape Navigator sits on 86% of the territory, but Windows 95 now bundles Internet Explorer to quickly catch up. Facing this offensive, Netscape opensource its browser.
France falls behind. Outside universities, Internet access remains expensive and laborious. Minitel still reigns, offering phone directory, train tickets, remote shopping. This was not yet possible with the Internet: buying a CD online was a pipe dream. Encryption suffers from inappropriate regulation: the DES algorithm is capped at 40 bits and Continue reading

In the previous post, we covered PIM Dense Mode and mentioned that it is not widely used in production because of its flood and prune behaviour. Every router in the network receives the multicast traffic first, and then routers without interested receivers have to send prune messages. This is inefficient, especially in large networks.

In this post, we will look at PIM Sparse Mode, which takes the opposite approach. Instead of flooding traffic everywhere and pruning where it is not needed, Sparse Mode only sends traffic to parts of the network that explicitly request it. Routers with interested receivers send Join messages and only then does the multicast traffic start flowing. This makes Sparse Mode much more efficient and scalable, which is why it is the preferred mode in most production networks today.
In Dense Mode, we saw two main problems. Multicast traffic is flooded everywhere, and every router has to maintain state for every multicast group, even if all its interfaces are pruned. Sparse Mode Continue reading
In the previous EVPN/VXLAN lab exercises, we covered the basics of Ethernet bridging over VXLAN and the use of the EVPN control plane to build layer-2 segments.
It’s time to move up the protocol stack. Let’s see how you can route between VXLAN segments, this time using unique unicast IP addresses on the layer-3 switches.
You can run the lab on your own netlab-enabled infrastructure (more details), but also within a free GitHub Codespace or even on your Apple-silicon Mac (installation, using Arista cEOS container, using VXLAN/EVPN labs).

The future of network design and architecture is–based on current trends–is going to be working with and around resource constraints. How would resource constraints impact the way we design and manage networks? Mike Bushong joins Tom, Eyvonne, and Russ to ponder network engineering in a resource constrained world.
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In our previous post, we addressed the most common questions platform teams are asking as they prepare for the retirement of the NGINX Ingress Controller. With the March 2026 deadline fast approaching, this guide provides a hands-on, step-by-step walkthrough for migrating to the Kubernetes Gateway API using Calico Ingress Gateway. You will learn how to translate NGINX annotations into HTTPRoute rules, run both models side by side, and safely cut over live traffic.
The announced retirement of the NGINX Ingress Controller has created a forced migration path for the many teams that relied on it as the industry standard. While the Ingress API is not yet officially deprecated, the Kubernetes SIG Network has designated the Gateway API as its official successor. Legacy Ingress will no longer receive enhancements and exists primarily for backward compatibility.
While the Ingress API served the community for years, it reached a functional ceiling. Calico Ingress Gateway implements the Gateway API to provide:
Welcome to the 24th edition of Cloudflare’s Quarterly DDoS Threat Report. In this report, Cloudforce One offers a comprehensive analysis of the evolving threat landscape of Distributed Denial of Service (DDoS) attacks based on data from the Cloudflare network. In this edition, we focus on the fourth quarter of 2025, as well as share overall 2025 data.
The fourth quarter of 2025 was characterized by an unprecedented bombardment launched by the Aisuru-Kimwolf botnet, dubbed “The Night Before Christmas" DDoS attack campaign. The campaign targeted Cloudflare customers as well as Cloudflare’s dashboard and infrastructure with hyper-volumetric HTTP DDoS attacks exceeding rates of 200 million requests per second (rps), just weeks after a record-breaking 31.4 Terabits per second (Tbps) attack.
DDoS attacks surged by 121% in 2025, reaching an average of 5,376 attacks automatically mitigated every hour.
In the final quarter of 2025, Hong Kong jumped 12 places, making it the second most DDoS’d place on earth. The United Kingdom also leapt by an astonishing 36 places, making it the sixth most-attacked place.
Infected Android TVs — part of the Aisuru-Kimwolf botnet — bombarded Cloudflare’s network with hyper-volumetric HTTP DDoS attacks, while Telcos emerged as the most-attacked industry.
Receiver Credit-Based Congestion Control (RCCC) is a cornerstone of the Ultra Ethernet transport architecture, specifically designed to eliminate incast congestion. Incast occurs at the last-hop switch when the aggregate data rate from multiple senders exceeds the egress interface capacity of the target’s link. This mismatch leads to rapid buffer exhaustion on the outgoing interface, resulting in packet drops and severe performance degradation.
Figure 8-1 illustrates the operational flow of the RCCC algorithm. In a standard scenario without credit limits, source Rank 0 and Rank 1 might attempt to transmit at their full 100G line rates simultaneously. If the backbone fabric consists of 400G inter-switch links, the core utilization remains a comfortable 50% (200G total traffic). However, because the target host link is only 100G, the last-hop switch (Leaf 1B-1) becomes an immediate bottleneck. The switch is forced to queue packets that cannot be forwarded at the 100G egress rate, eventually triggering incast congestion and buffer overflows.
While "incast" occurs at the egress interface and can resemble head-of-line blocking, it is fundamentally a "fan-in" problem where multiple sources converge on a single receiver. Under RCCC, standard Explicit Congestion Notification (ECN) on the last-hop switch's egress interface is Continue reading