Book Image

Edge Computing Patterns for Solution Architects

By : Ashok Iyengar, Joseph Pearson
Book Image

Edge Computing Patterns for Solution Architects

By: Ashok Iyengar, Joseph Pearson

Overview of this book

Enriched with insights from a hyperscaler’s perspective, Edge Computing Patterns for Solution Architects will prepare you for seamless collaboration with communication service providers (CSPs) and device manufacturers and help you in making the pivotal choice between cloud-out and edge-in approaches. This book presents industry-specific use cases that shape tailored edge solutions, addressing non-functional requirements to unlock the potential of standard edge components. As you progress, you’ll navigate the archetypes of edge solution architecture from the basics to network edge and end-to-end configurations. You’ll also discover the weight of data and the power of automation for scale and immerse yourself in the edge mantra of low latency and high bandwidth, absorbing invaluable do's and don'ts from real-world experiences. Recommended practices, honed through practical insights, have also been added to guide you in mastering the dynamic realm of edge computing. By the end of this book, you'll have built a comprehensive understanding of edge concepts and terminology and be ready to traverse the evolving edge computing landscape.
Table of Contents (17 chapters)
Free Chapter
1
Part 1:Overview of Edge Computing as a Problem Space
4
Part 2: Solution Architecture Archetypes in Context
8
Part 3: Related Considerations and Concluding Thoughts

Manufacturing scenario

In an effort to make the manufacturing process more efficient, we see assembly-line robots, warehouse robots, acoustic calibrators, and industrial cameras inspecting flaws on the manufacturing line becoming more commonplace in the realm of industrial automation. From an edge computing perspective, these are all edge devices that run applications specific to the tasks they perform. We will look at a scenario that uses AI to detect anomalies or flaws in robotic welding, with the ultimate goal of preventing assembly-line stoppage. Typically, such quality checks are done manually by the quality control (QC) team, which adds time delays and could be costly.

The four groups of components in this scenario are:

  • The devices, including robotic welding components and ruggedized cameras on the shop floor
  • The edge-related platform components in the enterprise
  • 5G networking components and software
  • Services in the enterprise cloud

Enterprises have...