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IoT Edge Computing with MicroK8s

IoT Edge Computing with MicroK8s

By : Karthikeyan Shanmugam
4 (1)
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IoT Edge Computing with MicroK8s

IoT Edge Computing with MicroK8s

4 (1)
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)
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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

Essentials of IoT and Edge Computing

Data volumes continue to grow, particularly in industries such as manufacturing, oil and gas, energy, and transportation that are undergoing rapid digital transformation. This data explosion at the edge must be managed. There are numerous challenges involved with this, including system complexity, data privacy, latency issues, poor bandwidth connectivity, and rising costs for storing and processing data.

Edge computing reduces the amount of long-distance connectivity between a device and server by getting compute as close to the data source as possible, thereby improving the way the data is handled, processed, and delivered. When it comes to large industries such as manufacturing, oil and gas, energy, or transportation, industrial edge computing is being deployed to analyze and manage all data at the asset end in real time for real-time analytics or to use the aggregated data for further processing in the cloud.

The edge has three main components...

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IoT Edge Computing with MicroK8s
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