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

Creating services to monitor your devices in real time using GPS

In our use case, we are going to deploy a service that sends data from our edge device after some processing in the cloud. The goal of this use case is to have a global geolocation system for multiple vehicles delivering packages, showing their location in real time. For this, we are going to create a gps-server deployment that stores all the coordinates for our units in Redis and Mongo. We are going to use the Python Flask library to create this service. Let’s explore the main sections of the following pseudocode mixed with Python:

<imported libraries>
<app_initialization>
<CORS configuration>
 
def redisCon():
<return Redis connection object>
 
@app.route("/client/<cid>/position", methods=["POST"])
def setPosition(cid):
   <Call redisCon>
   <Store of data in a Redis hash data type using 
    the fields...