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Edge Computing Patterns for Solution Architects

You're reading from   Edge Computing Patterns for Solution Architects Learn methods and principles of resilient distributed application architectures from hybrid cloud to far edge

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781805124061
Length 214 pages
Edition 1st Edition
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Authors (2):
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Ashok Iyengar Ashok Iyengar
Author Profile Icon Ashok Iyengar
Ashok Iyengar
Joseph Pearson Joseph Pearson
Author Profile Icon Joseph Pearson
Joseph Pearson
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1:Overview of Edge Computing as a Problem Space FREE CHAPTER
2. Chapter 1: Our View of Edge Computing 3. Chapter 2: Edge Architectural Components 4. Part 2: Solution Architecture Archetypes in Context
5. Chapter 3: Core Edge Architecture 6. Chapter 4: Network Edge Architecture 7. Chapter 5: End-to-End Edge Architecture 8. Part 3: Related Considerations and Concluding Thoughts
9. Chapter 6: Data Has Weight and Inertia 10. Chapter 7: Automate to Achieve Scale 11. Chapter 8: Monitoring and Observability 12. Chapter 9: Connect Judiciously but Thoughtlessly 13. Chapter 10: Open Source Software Can Benefit You 14. Chapter 11: Recommendations and Best Practices 15. Index 16. Other Books You May Enjoy

Device configuration

In this section, we will cover the device configuration use case as an introduction to the edge device hub archetype pattern. You will learn the role and purpose of the infrastructure, platforms, and applications (collectively referred to as architectural elements going forward and shown in Figure 3.4) that support device operation at the edge. We will briefly discuss the benefits and drawbacks of placement decisions.

Rationale

The device configuration use case is meant to supplant legacy IoT architectures that connect fixed-function devices and embedded systems directly to a host in the cloud. Having a single platform for all devices puts the burden of device registration, protocol support, data collection, and analytics on one platform in one location. However, this approach required all data to be sent to the cloud if it wasn’t discarded, resulted in delayed analysis (thus delayed reaction times due to latency), and incurred data transfer and storage...

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