Making Sense of Edge Computing
Cody Bumgardner, Caylin Hickey
  • MEAP began April 2020
  • Publication in Spring 2021 (estimated)
  • ISBN 9781617297595
  • 350 pages (estimated)
  • printed in black & white

Much needed text that covers important edge computing topics in an approachable and systemized manner.

Milorad Imbra
Edge computing systems run essential data processing tasks on the devices that make up their network, reducing laggy data transfers and expensive cloud infrastructure. Edge systems can be complex, so it’s essential to have the big picture as you explore this innovative technology. Making Sense of Edge Computing gives you an easy-to-grok technical overview, covering what sets edge computing apart from other systems, and how to get started on edge projects from personal scale IoT to geographically distributed systems demanding real-time data processing.

About the Technology

In edge systems, individual devices gather and process their own data, delivering huge benefits for speed, legal adherence, and cost-efficiency. A self-driving car saves vital seconds by running its own pedestrian image recognition. A CCTV system preserves privacy with a de-identification program before transferring footage. A smart watch calculates fitness stats on its own circuits instead of incurring costs in the cloud. Edge systems are in demand from an ever-growing set of applications, and it’s important to understand the benefits and impacts this model can have on your work.

About the book

Making Sense of Edge Computing is a technical overview of edge computing systems offering accessible examples and clear explanations. You’ll start with what sets edge computing apart from other distributed systems. Then, you’ll dive into the various components of these systems, including the entity relationships, networks, and so forth, you’ll need to model your problem. You’ll learn about testing the performance of edge components, picking the right stack for your projects, and how to develop custom features. Finally, you’ll get a chance to test your skills by exploring three large-scale edge computing projects: an agent-based IoT system, a privacy-preserving app, and a geographically distributed system with a huge number of agents and vast volumes of data.
Table of Contents detailed table of contents

Part 1: An Introduction to Edge Computing

1 Introducing edge computing

1.1 What is this “edge” you speak of?

1.2 What makes edge computing different?

1.2.1 Client-server and cloud models

1.2.2 How can edge computing help

1.3 The components of edge computing

1.3.1 Distributed data and infrastructure

1.3.2 Data transport and integration service bus

1.3.3 Semantic data layer

1.3.4 Application layer

1.4 The edge is inevitable

1.4.1 The three laws of IoT

1.5 Example uses of edge computing

1.5.1 Gunshot detection in a smarter city

1.5.2 Managing patient alerts in a hospital

1.5.3 Hobbyist app: Personal fitness coach

1.6 Summary

2 Welcome to your edge testbed

2.1 Getting to know your edge environment

2.2 Launching an edge testbed

2.2.1 Docker for Windows: Docker Toolbox and Docker Desktop

2.2.2 Starting the edge container

2.2.3 Launching the agent

2.2.4 Shutting down the agent

2.3 Launching an edge application

2.3.1 Mounting a local directory in your Docker container

2.3.2 Downloading the example plugin

2.3.3 Using the testbed dashboard

2.3.4 Logging into the dashboard

2.3.5 Uploading the example plugin to the agent

2.3.6 Adding and configuring plugins in the application builder

2.3.7 Submitting your edge application

2.4 Building an edge application

2.4.1 Cloning the example plugin

2.4.2 Modifying the example plugin code

2.4.3 Compiling and building your customized filerepo plugin

2.4.4 Launching your custom application

2.5 Summary

Part 2: Fundamental Concepts

3 Agent-based systems

3.1 Hiring yourself a secret agent

3.1.1 All systems functional

3.1.2 Getting work done with agents

3.1.3 Let’s appify our edge functions

3.2 Arranging your agents

3.2.1 Simple single-layer topologies

3.2.2 Demo: Building a single-layer topology

3.2.3 Moving to the multi-layer

3.2.4 Demo: Adding another company to your warehouse

3.3 Secret agent communications

3.3.1 Message brokers to bind them all

3.4 When good agents go bad

3.4.1 An agent’s secret identity

3.4.2 Picking up after one another

3.4.3 What’s going on?

3.5 Use case: Bridging agents between home and the cloud

3.6 Summary

4 Streaming complex event processing

4.1 What is streaming data

4.1.1 Data in transit versus data at rest

4.1.2 Where are data streams typically found

4.2 What is complex event processing

4.2.1 What is event processing

4.2.2 Event processing involving time

4.2.3 When might you want to enrich a data stream?

4.2.4 When might you want to relate two events?

4.3 Complex event processing in edge computing

4.3.1 Downloading the CEP plugin

4.3.2 Launching your agent and dashboard

4.3.3 Building your CEP

4.3.4 Uploading the CEP plugin to the agent registry

4.3.5 Adding and configuring plugins in the application builder

4.3.6 Submitting your edge application

4.4 Summary

5 Applications as graphs

6 Dynamic edge-application management, monitoring, measurement, and scheduling

Part 3: Rolling Your Own: Edge Computing In The Real World

7 Building an agent-based IoT project (details TBD)

8 Big data, sensitive data, important data (details TBD)

9 Geographically distributed systems, large numbers of agents, big data (details TBD)

Part 4: Comercial Edge Platforms

10 AWS

11 Google

12 Azure



What's inside

  • Identify problems that can be solved by edge computing
  • Design edge-focused infrastructures
  • Develop and implement dynamic edge-enabled pipelines
  • Monitor and measure KPIs in distributed edge applications
  • Agent-based computing and autonomic systems
  • Popular edge computing frameworks from AWS, Google, and Azure

About the reader

For technology professionals familiar with distributed systems and cloud computing.

About the authors

Dr. Cody Bumgardener has over twenty years of experience working with the communications, distributed systems, and streaming complex event processing technology that make up edge computing. His research focuses on the applied use of edge computing, distributed inference, and AI in large-scale real-time systems. Caylin Hickey has over a decade’s experience in data analytics, cloud computing, distributed systems, and edge computing. He is pursuing his PhD in computer science at the University of Kentucky.

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