Book Image

Edge Computing Systems with Kubernetes

By : Sergio Méndez
Book Image

Edge Computing Systems with Kubernetes

By: Sergio Méndez

Overview of this book

Edge computing is a way of processing information near the source of data instead of processing it on data centers in the cloud. In this way, edge computing can reduce latency when data is processed, improving the user experience on real-time data visualization for your applications. Using K3s, a light-weight Kubernetes and k3OS, a K3s-based Linux distribution along with other open source cloud native technologies, you can build reliable edge computing systems without spending a lot of money. In this book, you will learn how to design edge computing systems with containers and edge devices using sensors, GPS modules, WiFi, LoRa communication and so on. You will also get to grips with different use cases and examples covered in this book, how to solve common use cases for edge computing such as updating your applications using GitOps, reading data from sensors and storing it on SQL and NoSQL databases. Later chapters will show you how to connect hardware to your edge clusters, predict using machine learning, and analyze images with computer vision. All the examples and use cases in this book are designed to run on devices using 64-bit ARM processors, using Raspberry Pi devices as an example. By the end of this book, you will be able to use the content of these chapters as small pieces to create your own edge computing system.
Table of Contents (21 chapters)
1
Part 1: Edge Computing Basics
7
Part 2: Cloud Native Applications at the Edge
13
Part 3: Edge Computing Use Cases in Practice

Technical requirements

To deploy the databases in this chapter, you need the following:

  • A single- or multi-node K3s cluster using ARM devices with MetalLB, and Longhorn storage installed. If you are using Raspberry Pi devices, you will need at least 4 GB of RAM and at least the 4B model. Each node must have the Ubuntu ARM64 operating system in order to support the ARMv8 architecture, necessary for some deployments in this chapter.
  • kubectl configured to be used on your local machine, to avoid using the --kubeconfig parameter.
  • Clone the repository at https://github.com/PacktPublishing/Edge-Computing-Systems-with-Kubernetes/tree/main/ch10 if you want to run the YAML configuration by using kubectl apply instead of copying the code from the book. Take a look at the directory yaml for the YAML examples inside the ch10 directory. 

With this, you can deploy the databases explained in this chapter. So, let’s get started learning about CAP theorem first, to...