High-Performance Kubernetes Networking with Calico eBPF

Kubernetes has revolutionized cloud-native applications, but networking remains a crucial aspect of ensuring scalability, security, and performance. Default networking approaches, such as iptables-based packet filtering, often introduce performance bottlenecks due to inefficient packet processing and complex rule evaluations. This is where Calico eBPF comes into play, offering a powerful alternative that enhances networking efficiency and security at scale.

Understanding Kubernetes Networking

Kubernetes networking consists of two primary components:

  1. Physical Network Infrastructure – Connects cloud resources to external networks, ensuring communication between nodes and the broader internet.
  2. Cluster Network Infrastructure – Manages internal workload communication within the Kubernetes cluster, including service-to-service traffic and pod-to-pod interactions.

Choosing the right data plane is critical for optimal performance. Factors such as cluster size, throughput, and security requirements should guide this choice. Poor networking choices can lead to congestion, excessive latency, and resource starvation.

Data Plane Options in Kubernetes Networking

Networking in Kubernetes is an abstract idea. While Kubernetes lays the foundation, your Container Networking Interface (CNI) is in charge of the actual networking. To better understand networking, we usually divide it into two sections: a control plane and a data plane.

Arista Can Ride AI Up Past $10 Billion In 2026

In many ways, Arista Networks still behaves like a startup even though it was founded twenty years ago, rollout out its first products a little more than a decade and a half ago, went public a decade ago, and now as over 10,000 customers and over 100 million Ethernet ports sold that generated a cumulative $32 billion in revenues for hardware, software, and support. …

Arista Can Ride AI Up Past $10 Billion In 2026 was written by Timothy Prickett Morgan at The Next Platform.

Python Getters, Setters and @property Decorator

Python Getters, Setters and @property Decorator

When I first started working with Python classes, some of the most confusing topics were getters, setters, and @property. There are plenty of tutorials on how to use them, but very few actually explain why do we need them or what problem do they solve. So, I thought I’d write a dedicated post covering what they are and the problems they solve. Let’s get to it.

As always, if you find this post helpful, press the ‘clap’ button. It means a lot to me and helps me know you enjoy this type of content.

Python OOP - Method vs Function and the Mystery of ‘self’
I just realized how much I didn’t understand about Python Object-Oriented Programming. I thought I knew the basics, but a few days ago, while going through a Python course, I found out I was wrong.
Python Getters, Setters and @property DecoratorPacketswitchSuresh Vina
Python Getters, Setters and @property Decorator

Python Classes

Before diving in, let's have a quick look at a Python class. Here’s a simple example of a Person class with two attributes name and age.

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

I'm going to create an instance of the class called p1, passing Continue reading

Do You Need To Answer That Question?

Photo by Pixabay on Pexels.com

We’ve all been in a situation where we’re listening to a presentation or in a class where someone is sharing knowledge. The presenter or expert finishes a point and stops to take a breath or move on to the next point when you hear a voice.

“What they meant to say was…”

You can already picture the person doing it. I don’t need to describe the kind of person that does this. We all know who it is and, if you’re like me, it drives you crazy. I know it because I’ve found myself being that person several times and it’s something I’m working hard to fix.

Info to Share

People that want to chime in feel like they have important things to share. Maybe they know something deeper about the subject. Perhaps they’ve worked on a technology and have additional information to add to the discussion. They mean well. They’re eager to add to the discussion. They mean well. Most of the time.

What about the other times? Maybe it’s someone that thinks they’re smarter than the presenter. I know I’ve had to deal with that plenty of times. It could be an Continue reading

Goodbye, Cumulus Community Vagrant Boxes

Last Monday, I decided to review and merge the “VXLAN on Cumulus Linux 5.x with NVUE” pull request. I usually run integration tests on the modified code to catch any remaining gremlins, but this time, all the integration tests started failing during the VM creation phase. I was completely weirded out, considering everything worked a week ago.

Fortunately, Vagrant debugging is pretty good1 and I was quickly able to pinpoint the issue (full printout):

Read more …

Network Automation and AI at NANOG 93

The dominant theme of the presentations this time around at NANOG 93 was the combination of automation of network command and control and the application of Artificial Intelligence tools to this control function. The interest in AI appears to have heightened of late, and while the hype levels are impressive even for an industry that can get totally fixated on hype, the deliverables so far still appear to fall somewhat short.

PP050: Understanding–and Protecting Yourself From–the Malware Economy

There’s a robust malware economy with an active market for exploits, brokered access to compromised systems, ransomware, bots-as-a-service, and more. And this malware economy is targeted at you. On today’s Packet Protector we talk with Jake Williams, a security researcher, consultant, and instructor, about the malware economy, how it operates, and the most effective strategies... Read more »

Tech Bytes: Nokia Event-Driven Automation: Simple, Reliable Data Center Automation (Sponsored)

Nokia’s Event-Driven Automation, or EDA, is a network automation platform that aims to help network engineers achieve predictable, error-free operations so you can keep up with all the change tickets coming your way while ensuring the data center is reliable and performant. On today’s Tech Bytes podcast, sponsored by Nokia, we’ll talk about how EDA... Read more »

NB514: Cisco, Juniper Announce New Switches; SolarWinds Goes Private in $4.4 Billion Buyout

Take a Network Break! We start with some Red Alert vulnerabilities to get your blood pumping, and then dive into networking news. Cisco announces new data center switches with AMD Pensando DPUs that let you deploy security and other services directly on to the switch. Cisco ThousandEyes is previewing Traffic Insights, which correlates flow records... Read more »

HS095: The Journey to a Self-Healing Network: Intelligence, Agents, and Complexity (Sponsored)

Can AI and automation create a truly autonomous network, one that’s self-diagnosing and self-healing? Join Vitria CTO and Founder Dale Skeen and industry analyst Charlotte Patrick in this sponsored episode of Heavy Strategy to discuss the challenges–and limitations–of using AI to create autonomous networking. This discussion covers the “intelligence architecture” required to implement automation, and ... Read more »

Loopback as a Service

Methods of steering traffic into MPLS and Segment Routing LSP is one of the least standardized and most confusing parts of traffic engineering.

The mess of nexthop resolution

Despite some existing interoperability issues, in general, the MPLS and SR control …

BalticNOG Meeting (September 2025)

Donatas Abraitis asked me to spread the word about the first ever Baltic NOG meeting in the second half of September 2025 (more details)

If you were looking for a nice excuse to visit that part of Europe (it’s been on my wish list for a very long time), this might be a perfect opportunity to do it 😎.

On a tangential topic of fascinating destinations 😉, there’s also ITNOG in Bologna (May 19th-20th, 2025), Autocon in Prague (May 26th-30th, 2025), and SWINOG in Bern (late June 2025).

Read more …

Helping civil society monitor attacks with the CyberPeaceTracer and Cloudflare Email Security

Civil society organizations have always been at the forefront of humanitarian relief efforts, as well as safeguarding civil and human rights. These organizations play a large role in delivering services during crises, whether it is fighting climate change, support during natural disasters, providing health services to marginalized communities and more. 

What do many of these organizations have in common? Many times, it’s cyber attacks from adversaries looking to steal sensitive information or disrupt their operations. Cloudflare has  seen this firsthand when providing free cybersecurity services to vulnerable groups through programs like Project Galileo, and found that in aggregate, organizations protected under the project experience an average of 95 million attacks per day. While cyber attacks are a problem across all industries in the digital age, civil society organizations are disproportionately targeted, many times due to their advocacy, and because attackers know that they typically operate with limited resources. In most cases, these organizations don’t even know they have been attacked until it is too late. 

Over the last 10 years of Project Galileo, we’ve had the opportunity to work more closely with leading civil society organizations. This has led to a number of exciting new partnerships, Continue reading

Large Language Model (LLM) – Part 2/2: Transformer Architecture

 Introduction

Sequence-to-sequence (seq2seq) language translation and Generative Pretrained Transformer (GPT) models are subcategories of Natural Language Processing (NLP) that utilize the Transformer architecture. Seq2seq models are typically using Long Short-Term Memory (LSTM) networks or encoder-decored based Transformers. In contrast, GPT is an autoregressive language model that uses decoder-only Transformer mechanism. The purpose of this chapter is to provide an overview of the decoder-only Transformer architecture.

The Transformer consists of stacks of decoder modules. A word embedding vector, a result of the word tokenization and embbeding, is fed as input to the first decoder module. After processing, the resulting context vector is passed to the next decodeer, and so on. After the final decoder, a softmax layer evaluates the output against the complete vocabulary to predict the next word. As an autoregressive model, the predicted word vector from the softmax layer is converted into a token before being fed back into the subsequent decoder layer. This process involves a token-to-word vector transformation prior to re-entering the decoder.

Each decoder module consists of an attention layer, Add & Normalization layer and a feedforward neural network (FFNN). Rather than feeding the embedded word vector (i.e., token embedding plus positional encoding) directly Continue reading

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