Yesterday’s blog post discussed the traffic flow and the routing information flow in a hub-and-spoke VPN design (a design in which all traffic between spokes flows through the hub site). It’s time to implement and test it, starting with the simplest possible scenario: a single PE router using inter-VRF route leaking to connect the VRFs.
I know what you're thinking, we usually manage our Python code via Git to track changes, but what do I mean by using GitPython to manage Git repositories? I recently faced a situation where I needed to automate a Git workflow. This includes pulling the latest changes from a Git repository, creating a branch, making some changes, viewing the diff, committing, and then pushing my branch back to the remote repository.
Doing this repeatedly was time-consuming, and I figured there must be a way to automate this. With Python, virtually anything is possible. I found a Python library called 'GitPython' that does exactly this. So, let's get to it.
GitPython is a Python library that lets you work with Git repositories. It allows you to manage Git tasks using Python code, making it easy to automate things like commits, branches, and pushes without using the command line. This is useful for automating repetitive Git tasks directly from Python.
For example, you can use it to pull the latest updates from a repository, create new branches, and commit to your changes. It also provides a way to view diffs, so you can see what has changed Continue reading
Hub-and-spoke topology is by far the most complex topology I’ve ever encountered in the MPLS/VPN (and now EVPN) world. It’s used when you want to push all the traffic between sites attached to a VPN (spokes) through a central site (hub), for example, when using a central firewall.
You get the following diagram when you model the traffic flow requirements with VRFs. The forward traffic uses light yellow arrows, and the return traffic uses dark orange ones.
Several books on artificial intelligence (AI) and deep learning (DL) have been published over the past decade. However, I have yet to find a book that explains deep learning from a networking perspective while providing a solid introduction to DL. My goal is to fill this gap by writing a book titled AI for Network Engineers (note that the title name may change during the writing process). Writing about such a complex subject will take time, but I hope to complete and release it within a year.
The first part of the book covers the theory behind Deep Learning. It begins by explaining the construct of a single artificial neuron and its functionality. Then, it explores various Deep Neural Network models, such as Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). Next, the first part discusses data and model parallelization strategies such as Data, Pipeline, and Tensor Parallelism, explaining how input data and/or model sizes that exceed the memory capacity of GPUs within a single server can be distributed across multiple GPU servers.
After a brief Continue reading
In the previous pipelines, I’ve been using Python and had to install multiple pip modules. Suppose we have 5 different jobs, we would be installing the pip modules again and again for each job, which takes a while to complete. Remember, each job runs in its own pristine environment, meaning it builds a fresh Docker container and installs all the required modules before running the script we need. This repetition can slow down the pipeline significantly.
In this blog post, let’s look at how you can use GitLab Cache to speed up your jobs and avoid unnecessary reinstallations. If you are new to GitLab or CI/CD in general, I highly recommend checking out my previous GitLab introduction post below.
This is how my pipeline looked before. Though it worked perfectly fine, it took around 45 seconds to run each job and just over 3 minutes for the entire pipeline to Continue reading
Artificial Intelligence (AI) is a broad term for solutions that aim to mimic the functions of the human brain. Machine Learning (ML), in turn, is a subset of AI, suitable for tasks like simple pattern recognition and prediction. Deep Learning (DL), the focus of this section, is a subset of ML that leverages algorithms to extract meaningful patterns from data. Unlike ML, DL does not necessarily require human intervention, such as providing structured, labeled datasets (e.g., 1,000 bird images labeled as “bird” and 1,000 cat images labeled as “cat”).
DL utilizes layered, hierarchical Deep Neural Networks (DNNs), where hidden and output layers consist of computational units, artificial neurons, which individually process input data. The nodes in the input layer pass the input data to the first hidden layer without performing any computations, which is why they are not considered neurons or computational units. Each neuron calculates a pre-activation value (z) based on the input received from the previous layer and then applies an activation function to this value, producing a post-activation output (ŷ) value. There are various DNN models, such as Feed-Forward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), each designed for different use cases. For example, FNNs are suitable for simple, structured tasks like handwritten digit recognition using the MNIST dataset [1], CNNs are effective for larger image recognition tasks such as with the CIFAR-10 dataset [2], and RNNs are commonly used for time-series forecasting, like predicting future sales based on historical sales data.
To provide accurate predictions based on input data, neural networks are trained using labeled datasets. The MNIST (Modified National Institute of Standards and Technology) dataset [1] contains 60,000 training and 10,000 test images of handwritten digits (grayscale, 28x28 pixels). The CIFAR-10 [2] dataset consists of 60,000 color images (32x32 pixels), with 50,000 training images and 10,000 test images, divided into 10 classes. The CIFAR-100 dataset [3], as the name implies, has 100 image classes, with each class containing 600 images (500 training and 100 test images per class). Once the test results reach the desired level, the neural network can be deployed to production.
We all know that by default, all the devices in the same VLAN can talk to each other. For example, if you have a switch with multiple devices connected to it and if they are part of the same VLAN, they can communicate without any restrictions. But there are times when you might want to keep the devices in the same VLAN while preventing them from talking to each other. This is where Private VLANs come into play, offering control over who can talk to each other within the 'same VLAN'. So, let’s get started and we will cover the following topics.
Let's break down how Private VLANs work with a simple scenario. Imagine we have a "users" VLAN where all the laptops connect. Suppose we have a mix of Windows and Linux devices. We want to ensure that Windows devices can't communicate with each other at all. However, it's okay for Linux devices to talk to each other, but they shouldn't communicate with the Windows devices either.
A quick reminder in case you were on vacation in late July: I published a short guide to creating netlab reports. Hope you’ll find it useful.
The cellular network world is similar enough to the IP networking world to feel familiar, but different enough to require learning new terms and ideas. Tom Nadeau joins Tom Ammon and Russ White to discuss one element of this networking world, the RAN network, and the current move towards open source and white box disaggregated solutions.
Consider the case of a malicious actor attempting to inject, scrape, harvest, or exfiltrate data via an API. Such malicious activities are often characterized by the particular order in which the actor initiates requests to API endpoints. Moreover, the malicious activity is often not readily detectable using volumetric techniques alone, because the actor may intentionally execute API requests slowly, in an attempt to thwart volumetric abuse protection. To reliably prevent such malicious activity, we therefore need to consider the sequential order of API requests. We use the term sequential abuse to refer to malicious API request behavior. Our fundamental goal thus involves distinguishing malicious from benign API request sequences.
In this blog post, you’ll learn about how we address the challenge of helping customers protect their APIs against sequential abuse. To this end, we’ll unmask the statistical machine learning (ML) techniques currently underpinning our Sequence Analytics product. We’ll build on the high-level introduction to Sequence Analytics provided in a previous blog post.
Introduced in the previous blog post, let’s consider the idea of a time-ordered series of HTTP API requests initiated by a specific user. These occur as the user interacts with a service, such as while browsing Continue reading
Much has changed in the 2024 United States presidential election since the June 27 debate between Donald Trump and Joe Biden, then the presumptive nominees for the November election. Now, over two months later, on September 10, the debate was between Kamala Harris, the Democratic nominee, and Donald Trump, the Republican nominee. In this post, we will explore the event's impact on Internet traffic in specific states where there was a bigger impact than during the Biden-Trump debate, as well as examine cyberattacks, email phishing trends, and general DNS data on candidates, news, and election-related activity.
We’ve been tracking the 2024 elections globally through our blog and election report on Cloudflare Radar, covering some of the more than 60 national elections this year. Regarding the US elections, we have previously reported on trends surrounding the first Biden vs. Trump debate, the attempted assassination of Trump, the Republican National Convention, and the Democratic National Convention.
Typically, we have observed that election days don’t come with significant changes to Internet traffic, and the same is true for debates. Yet, debates can also draw attention that impacts traffic, especially when there is heightened anticipation. The 2024 debates were not only Continue reading