Book Image

IoT Edge Computing with MicroK8s

By : Karthikeyan Shanmugam
Book Image

IoT Edge Computing with MicroK8s

By: Karthikeyan Shanmugam

Overview of this book

Are you facing challenges with developing, deploying, monitoring, clustering, storing, securing, and managing Kubernetes in production environments as you're not familiar with infrastructure technologies? MicroK8s - a zero-ops, lightweight, and CNCF-compliant Kubernetes with a small footprint is the apt solution for you. This book gets you up and running with production-grade, highly available (HA) Kubernetes clusters on MicroK8s using best practices and examples based on IoT and edge computing. Beginning with an introduction to Kubernetes, MicroK8s, and IoT and edge computing architectures, this book shows you how to install, deploy sample apps, and enable add-ons (like DNS and dashboard) on the MicroK8s platform. You’ll work with multi-node Kubernetes clusters on Raspberry Pi and networking plugins (such as Calico and Cilium) and implement service mesh, load balancing with MetalLB and Ingress, and AI/ML workloads on MicroK8s. You’ll also understand how to secure containers, monitor infrastructure and apps with Prometheus, Grafana, and the ELK stack, manage storage replication with OpenEBS, resist component failure using a HA cluster, and more, as well as take a sneak peek into future trends. By the end of this book, you’ll be able to use MicroK8 to build and implement scenarios for IoT and edge computing workloads in a production environment.
Table of Contents (24 chapters)
1
Part 1: Foundations of Kubernetes and MicroK8s
4
Part 2: Kubernetes as the Preferred Platform for IOT and Edge Computing
7
Part 3: Running Applications on MicroK8s
14
Part 4: Deploying and Managing Applications on MicroK8s
21
Frequently Asked Questions About MicroK8s

Going Serverless with Knative and OpenFaaS Frameworks

In the last chapter, we discussed Kubeflow, which provides an easy-to-deploy, simple-to-use toolchain for data scientists to integrate the various resources they will need to run models on Kubernetes, such as Jupyter notebooks, Kubernetes deployment files, and machine learning libraries such as PyTorch and TensorFlow.

By using Kubeflow’s built-in Notebooks services, you can create notebooks and share them with your teams. We also went over how to set up a machine learning pipeline to develop and deploy an example model using the Kubeflow machine learning platform. Additionally, we established that Kubeflow on MicroK8s is simple to set up and configure, lightweight, and capable of simulating real-world conditions while building, migrating, and deploying pipelines.

In this chapter, we will look at the most popular open source serverless frameworks that extend Kubernetes with components for deploying, operating, and...