Skip to main content
Redhat Developers  Logo
  • AI

    Get started with AI

    • Red Hat AI
      Accelerate the development and deployment of enterprise AI solutions.
    • AI learning hub
      Explore learning materials and tools, organized by task.
    • AI interactive demos
      Click through scenarios with Red Hat AI, including training LLMs and more.
    • AI/ML learning paths
      Expand your OpenShift AI knowledge using these learning resources.
    • AI quickstarts
      Focused AI use cases designed for fast deployment on Red Hat AI platforms.
    • No-cost AI training
      Foundational Red Hat AI training.

    Featured resources

    • OpenShift AI learning
    • Open source AI for developers
    • AI product application development
    • Open source-powered AI/ML for hybrid cloud
    • AI and Node.js cheat sheet

    Red Hat AI Factory with NVIDIA

    • Red Hat AI Factory with NVIDIA is a co-engineered, enterprise-grade AI solution for building, deploying, and managing AI at scale across hybrid cloud environments.
    • Explore the solution
  • Learn

    Self-guided

    • Documentation
      Find answers, get step-by-step guidance, and learn how to use Red Hat products.
    • Learning paths
      Explore curated walkthroughs for common development tasks.
    • Guided learning
      Receive custom learning paths powered by our AI assistant.
    • See all learning

    Hands-on

    • Developer Sandbox
      Spin up Red Hat's products and technologies without setup or configuration.
    • Interactive labs
      Learn by doing in these hands-on, browser-based experiences.
    • Interactive demos
      Click through product features in these guided tours.

    Browse by topic

    • AI/ML
    • Automation
    • Java
    • Kubernetes
    • Linux
    • See all topics

    Training & certifications

    • Courses and exams
    • Certifications
    • Skills assessments
    • Red Hat Academy
    • Learning subscription
    • Explore training
  • Build

    Get started

    • Red Hat build of Podman Desktop
      A downloadable, local development hub to experiment with our products and builds.
    • Developer Sandbox
      Spin up Red Hat's products and technologies without setup or configuration.

    Download products

    • Access product downloads to start building and testing right away.
    • Red Hat Enterprise Linux
    • Red Hat AI
    • Red Hat OpenShift
    • Red Hat Ansible Automation Platform
    • See all products

    Featured

    • Red Hat build of OpenJDK
    • Red Hat JBoss Enterprise Application Platform
    • Red Hat OpenShift Dev Spaces
    • Red Hat Developer Toolset

    References

    • E-books
    • Documentation
    • Cheat sheets
    • Architecture center
  • Community

    Get involved

    • Events
    • Live AI events
    • Red Hat Summit
    • Red Hat Accelerators
    • Community discussions

    Follow along

    • Articles & blogs
    • Developer newsletter
    • Videos
    • Github

    Get help

    • Customer service
    • Customer support
    • Regional contacts
    • Find a partner

    Join the Red Hat Developer program

    • Download Red Hat products and project builds, access support documentation, learning content, and more.
    • Explore the benefits

Edge computing: From 30 tons to 30 grams

February 27, 2023
Don Schenck
Related topics:
Edge computing
Related products:
Red Hat Enterprise Linux

    When the ENIAC computer was introduced in 1946, it was housed in a huge room—1,800 square feet—and weighed 30 tons. It had to be assembled in place, and it wasn't going to be moved. The era of electronic computers had arrived, but only for an elite few. The idea of edge computing was science fiction—unbelievable science fiction at that. My, how things have changed.

    Mainframes

    The IBM mainframe computers, introduced in 1952, became the standard of computing for corporations and government agencies in the 1960s and 1970s. Those of us old enough can remember, for example, getting their home water bill in the form of a punched card with the words "Do not fold, spindle or mutilate" on it. These mainframe computers moved processing to the corporate headquarters. Sales from cash registers, for example, would be sent to headquarters on punched paper tape where it could be read into the mainframes for reporting.

    (Author's note: My first job in IT was processing paper tape into a mainframe.)

    Midrange computers

    In the 1970s, minicomputers (also called midrange computers) became very popular. The Digital VAX, Data General Nova, and the hugely popular IBM System/3x-400 series (System/3, System/32, System/34/, System/36, System/38, and AS/400) moved computing power even closer to the action. Midrange systems started in air-conditioned rooms with raised flooring, then moved to corners in offices, then eventually under desks as they grew in power and shrunk in physical size. Remote offices and small businesses now had a computer in their building. Larger midrange computers started to move into the spaces formerly occupied by the mainframes.

    The PC

    The 1980s saw the dawn of the desktop PC, and the trend of computing moving closer and closer to the action continued. A small desktop PC, for example, could be integrated into a production environment on a factory floor to record data and control machines. The PC would, typically, send the data to a host—often a midrange computer—and, likewise, get data from the host. This happened over a network: Twinax or ethernet cabling and the associated protocol—SNA, Novell Netware, and ethernet were the choices.

    Portables

    Adam Osborne brought the popular portable PC to the world in 1981 with the 24.5-pound Osborne 1. While more "luggable" than portable, this enabled the computer to move around more easily. Again, the processing power was moving closer to the action.

    Laptops and notebook computers followed and continue to evolve to this day, with a WiFi connection now being a requirement.

    Tablets and phones

    In the early 1990s, the personal digital assistant, or PDA, arrived. The star was the Palm Pilot, a small device that could store and record information. Users could take notes, make voice recordings, or manage their schedules. This machine was synched to a PC via a cable. This meant, while not real-time, processing could now be carried around in a pocket.

    The PDA was the genesis of and gave way to today's "must-have" item, the smartphone. Using WiFi and wireless 5G technology, the smartphone enables real-time data processing in a small form factor. This is one example of computing at the edge.

    Likewise, small tablets such as the iPad allow users to carry processing power with them, along with ample screen real estate and advanced communication functionality.

    Embedded systems and closer to the edge

    But it goes further, and deeper.

    Very small sensors and controllers can now be embedded into everyday items. These Internet of Things (IoT) devices range from thermostats to watches—the Apple watch weighs in at just over 30 grams, hence the title of this article—to, well, almost everything. You can even buy a ring that senses and reports data.

    These IoT devices might (or might not) have some computing ability beyond just collecting data, but they do often communicate with an edge computer or a cloud-based system that has advanced capabilities. Processing that occurs at or near the edge is where the greatest challenges are presented. Things such as enforcing security and installing updates are paramount because this is where the action takes place.

    All of this makes up "the edge." Sensing and processing move closer and closer to where the events occur. Communication capabilities have also improved, with WiFi, 5G, NFC, and more making it easier and more likely that edge devices will communicate with each other. An in-car network, for example, can improve automotive travel; Red Hat is working with GM on that very technology.

    Edge computing example: The modern automobile

    Let's consider the advanced automobile as a use case for edge computing.

    Sensors are used to report current speed, location, road conditions, outside temperature, lane edges, surrounding vehicles, and much more. These readings are reported to the driver and the drivetrain. The driver can use the information to make decisions, while an onboard computer can use the data to make adjustments—keep the car within the lanes, reduce speed based on front-facing radar, reduce torque based on road conditions, and much more.

    They could be expanded when "smart highways" are introduced. Sensors can keep track of traffic density and speed; accidents can be used as data, reporting to cars to determine speeds and, perhaps, a new route based on congestion.

    All this edge computing will need to be secure, and systems will need to be updated. We already have cars that can receive software updates while offline, i.e., not on the road. I can check the fuel level of my MINI from my smartphone.

    The future: Bright or dark?

    This is all just a start, only the beginning of more advanced systems running closer and closer to the action. One can easily wonder: Is Kurzweil's Singularity at hand?

    The future, truly, is at the edge.

    Last updated: August 14, 2023

    Related Posts

    • Developing at the edge: Best practices for edge computing

    • 5 things developers should know about edge computing

    • IoT edge development and deployment with containers through OpenShift: Part 1

    • Using Agile Integration for IoT

    Recent Posts

    • Every layer counts: Defense in depth for AI agents with Red Hat AI

    • Fun in the RUN instruction: Why container builds with distroless images can surprise you

    • Trusted software factory: Building trust in the agentic AI era

    • Build a zero trust AI pipeline with OpenShift and RHEL CVMs

    • Red Hat Hardened Images: Top 5 benefits for software developers

    What’s up next?

    Share Image

    This cheat sheet gets you started using Ansible to automate tasks on Cisco Meraki, an enterprise-cloud managed networking solution for managing access points, security appliances, L2 and L3 switches, and more.

    Get the cheat sheet
    Red Hat Developers logo LinkedIn YouTube Twitter Facebook

    Platforms

    • Red Hat AI
    • Red Hat Enterprise Linux
    • Red Hat OpenShift
    • Red Hat Ansible Automation Platform
    • See all products

    Build

    • Developer Sandbox
    • Developer tools
    • Interactive tutorials
    • API catalog

    Quicklinks

    • Learning resources
    • E-books
    • Cheat sheets
    • Blog
    • Events
    • Newsletter

    Communicate

    • About us
    • Contact sales
    • Find a partner
    • Report a website issue
    • Site status dashboard
    • Report a security problem

    RED HAT DEVELOPER

    Build here. Go anywhere.

    We serve the builders. The problem solvers who create careers with code.

    Join us if you’re a developer, software engineer, web designer, front-end designer, UX designer, computer scientist, architect, tester, product manager, project manager or team lead.

    Sign me up

    Red Hat legal and privacy links

    • About Red Hat
    • Jobs
    • Events
    • Locations
    • Contact Red Hat
    • Red Hat Blog
    • Inclusion at Red Hat
    • Cool Stuff Store
    • Red Hat Summit
    © 2026 Red Hat

    Red Hat legal and privacy links

    • Privacy statement
    • Terms of use
    • All policies and guidelines
    • Digital accessibility

    Chat Support

    Please log in with your Red Hat account to access chat support.