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

Existing hardware for your projects

There is plenty of hardware that you can use for your edge computing projects. Let’s look at a small list of hardware that you can use for your projects. The following list includes microcomputers such as the Raspberry Pi and microcontrollers such as Arduino:

  • Coral Dev Board: This is a board designed by Google that uses the Coral Accelerator to run ML applications. It is a reasonable size and provides processing power to run machine learning applications. For more information, check out https://coral.ai/products/dev-board.
  • Rock Pi: This device is similar to a Raspberry Pi but includes a Mali GPU, which can be used to process machine learning applications. It also has other board versions that you can use to run at the edge. For more information, check out https://rockpi.org.
  • Pine64: This is a community platform that creates boards that have ARM processors. It also has another product that can be used at the edge, similar to...