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

Automation with AI

We discussed deploying AI applications and ML models at the edge in Chapter 5, which is becoming a common scenario because enterprises are adamant about reducing the time for decision-making and minimizing data movement. In Chapter 4, we touched upon using AI/ML applications to determine network traffic patterns and using automation to perform network maintenance and monitor network performance. This latter discussion, albeit brief, is more in keeping with the automation theme.

Using AI techniques to automate facets of the edge computing paradigm will allow for automation at scale. With so much data being generated by edge devices, enterprises are finding ways to not only infer and analyze that data but also create a corpus that can be used to learn from, build, and train new models. We now see the rise of such corpus models as Large Language Models (LLMs).

LLMs and generative AI

LLMs are massive amounts of data gathered from numerous existing sources that...