Calico Ingress Gateway: Key FAQs Before Migrating from NGINX Ingress Controller

What Platform Teams Need to Know Before Moving to Gateway API

We recently sat down with representatives from 42 companies to discuss a pivotal moment in Kubernetes networking: the NGINX Ingress retirement.

With the March 2026 retirement of the NGINX Ingress Controller fast approaching, platform teams are now facing a hard deadline to modernize their ingress strategy. This urgency was reflected in our recent workshop, “Switching from NGINX Ingress Controller to Calico Ingress Gateway” which saw an overwhelming turnout, with engineers representing a cross-section of the industry, from financial services to high-growth tech startups.

During the session, the Tigera team highlighted a hard truth for platform teams: the original Ingress API was designed for a simpler era. Today, teams are struggling to manage production traffic through “annotation sprawl”—a web of brittle, implementation-specific hacks that make multi-tenancy and consistent security an operational nightmare.

The move to the Kubernetes Gateway API isn’t just a mandatory update; it’s a graduation to a role-oriented, expressive networking model. We’ve previously explored this shift in our blogs on Understanding the NGINX Retirement and Why the Ingress NGINX Controller is Dead.

Bridging the Role Gap: Transitioning from the flat, annotation-heavy Ingress model to the role-oriented Continue reading

TACC Explores Mixed Precision And FP64 Emulation For HPC With Horizon

If you want to test out an idea in HPC simulation and modeling and see how it affects a broad array of scientific applications, there is probably not a better place than the Texas Advanced Computing Center at the University of Texas. …

TACC Explores Mixed Precision And FP64 Emulation For HPC With Horizon was written by Timothy Prickett Morgan at The Next Platform.

HW070: Better Understand Your Network Performance with NetViews

Every Wi-fi or network professional occasionally struggles with understanding what their endpoints are experiencing. Keith sits down with Bill Bushong, creator of NetViews, a macOS application originally called PingStalker. In this conversation they discuss why he built NetViews, the technical details on how it works, its network monitoring capabilities, and how Wi-Fi professionals can use... Read more »

Improve global upload performance with R2 Local Uploads

Today, we are launching Local Uploads for R2 in open beta. With Local Uploads enabled, object data is automatically written to a storage location close to the client first, then asynchronously copied to where the bucket lives. The data is immediately accessible and stays strongly consistent. Uploads get faster, and data feels global.

For many applications, performance needs to be global. Users uploading media content from different regions, for example, or devices sending logs and telemetry from all around the world. But your data has to live somewhere, and that means uploads from far away have to travel the full distance to reach your bucket.

R2 is object storage built on Cloudflare's global network. Out of the box, it automatically caches object data globally for fast reads anywhere — all while retaining strong consistency and zero egress fees. This happens behind the scenes whether you're using the S3 API, Workers Bindings, or plain HTTP. And now with Local Uploads, both reads and writes can be fast from anywhere in the world.

Try it yourself in this demo to see the benefits of Local Uploads.

Ready to try it? Enable Local Uploads in the Cloudflare Dashboard under your bucket's settings, or Continue reading

Interface MAC Address in IOS Layer-2 Images

Here’s another “You can’t make this up, but it sounds too crazy to be true” story: Cisco IOS layer-2 images change the interface MAC address when you change the interface switchport status.

Let me start with a bit of background:

  • IOL Layer 2 image starts with interfaces enabled and in bridged (switchport) mode (details)
  • netlab has to run a normalize script (applicable to IOLL2, IOSv L2, and Arista EOS) before configuring anything else to ensure all interfaces are shut down.
  • The IOLL2 normalize Jinja template had a bug – when setting the interface MAC address, it checked l.mac_address instead of intf.mac_address. Nevertheless, everything worked because the MAC addresses were also set during the initial device configuration.
Read more …

NB560: Microsoft Doubles Down on Custom AI Chip; CrowdStrike Brandishes Big Bucks for Browser Security

Take a Network Break! We’ve got Red Alerts for HPE Juniper Session Smart Routers and SolarWinds. In this week’s news, Microsoft debuts its second-generation AI inferencing chip, Mplify rolls out a new Carrier Ethernet certification for supporting AI workloads, and AWS upgrades its network firewall to spot GenAI application traffic and filter Web categories. Google... Read more »

S3 is the new network: Rethinking data architecture for the cloud era

For decades, distributed databases have been built around the assumption that storage will live close to compute. The farther data travels over the network, the reasoning goes, the greater the potential for delay. Local RAID (redundant array of independent disks) arrays, network-attached storage (NAS), and cluster file systems keep data close, making it quick and easy to access. But in a distributed system, keeping the entire data store close to compute makes scaling slow, cumbersome, and expensive. Each time a node or cluster is replicated, its associated data must be replicated as well. It isn’t ideal, but until recently, there wasn’t any reasonable alternative. Databases had to scale. Service-level agreements (SLAs) had to be met. Wide-area networks weren’t reliable enough to support high-performance databases at scale. Database designers accordingly spent a great deal of energy solving problems related to coordination, consistency, and replication logic. But imagine things were different. What if they didn’t have to worry about the network, where their data lived, or how to get it from Point A to Point B? How would they design a database then? That’s the intriguing question raised by the advent of cloud object storage services like AWS S3, Google Cloud Continue reading

Fast FRR Container Configuration

After creating the infrastructure that generates the device configuration files within netlab (not in an Ansible playbook), it was time to try to apply it to something else, not just Linux containers. FRR containers were the obvious next target.

netlab uses two different mechanisms to configure FRR containers:

  • Data-plane features are configured with bash scripts using ip commands and friends.
  • Control-plane features are configured with FRR’s vtysh

I wanted to replace both with Linux scripts that could be started with the docker exec command.

Read more …

Ultra Ethernet: NSCC Destination Flow Control

Figure 6-14 depicts a demonstrative event where Rank 4 receives seven simultaneous flows (1). As these flows are processed by their respective PDCs and handed over to the Semantic Sublayer (2), the High-Bandwidth Memory (HBM) Controller becomes congested. Because HBM must arbitrate multiple fi_write RMA operations requiring concurrent memory bank access and state updates, the incoming packet rate quickly exceeds HBM’s transactional retirement rate. 

This causes internal buffers at the memory interface to fill, creating a local congestion event (3). To prevent buffer overflow, which would lead to dropped packets and expensive RMA retries, the receiver utilizes NSCC to move the queuing "pain" back to the source. This is achieved by using pds.rcv_cwnd_pend parameter of the ACK_CC header (4). The parameter operates on a scale of 0 to 127; while zero is ignored, a value of 127 triggers the maximum possible rate decrement. In this scenario, a value of 64 is utilized, resulting in a 50% penalty relative to the newly acknowledged data.

Rather than directly computing a new transport rate, the mechanism utilizes a three-phase process to define a restricted Congestion Window (CWND). This reduction in CWND inherently forces the source to drain its inflight bucket to Continue reading

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