How to get a deeper edge on edge computing

What is edge computing and why does it matter?

First, start with a recap of the basics. There is no single edge. I define edge computing as moving compute to locations as close as both feasible and necessary to the subscribers that need it. For example, the closest a telephone company can feasibly locate significant compute to its subscribers is their towers and network base stations. Organizations do this to minimize latency experienced by their customers, as well as alleviate bandwidth congestion on their upstream networks. In another example, for operational technology professionals it is often necessary to locate compute directly on the factory floor — close to the process — for reasons of bandwidth, latency, uptime and security. If you want a funny stare and an earful, ask the manager of a nuclear power plant to connect their operations up to the cloud. Bring popcorn.

Don’t get me wrong, the cloud isn’t going away, but commonly cited reasons for an increase in edge computing over time include reduced bandwidth consumption meaning cost savings, reduced latency through improved necessary reaction time to events in the physical world, maximum uptime by not relying on WAN like cellular that can be less reliable than wired, and improved security. In terms of security, this means protecting assets close to the source of data and in the legacy sense, ones that were never intended to connect to broader networks, much less the internet.

5G… another Windex of technology

Read “Getting to Advanced Class IoT” of my 5-part blog series for what I mean with the Windex reference. Often I get asked whether edge computing is a short-lived fad since 5G will negate the latency and bandwidth arguments. True in some cases, however uptime still matters. I wouldn’t trust my car airbag deployment to any WAN, regardless of how fast and reliable it is during normal operation. Further, bandwidth always comes with a cost.

Universal trust is paramount; we need an open edge to get there

All data originates at the edge, whether it’s generated by people or things. With people-generated data, you need to trust the individual who created it. After all, if it’s on the Internet, it has to be true, right? However, a benefit to deploying edge computing technology with IoT devices is the ability to apply intrinsic trust to data the moment it’s created in the physical world. This happens with the help of open, transparent technology including root of trust down to the silicon level.

The application of this trust to ensure data provenance and confidence everywhere can not only prepare us for what I call the holy grail of digital, but also meeting compliance needs, such as GDPR, as well as being able to trust your workloads running on the same infrastructure as others. I’ll continue to dig into these important topics in upcoming blogs.

What are the drawbacks of edge computing?

The biggest drawback to deploying edge technology is having to secure and manage all those distributed nodes, outside of the confines of secure data centers. For this to scale, you need consistent infrastructure regardless of devices, sensors or applications used. If you have a sweet tooth, you’ll appreciate it when I say that we need one cake with lots of flavors of icing and sprinkles.

For this reason, many great tech providers in the ecosystem have been investing in EdgeX Foundry as an open, vendor-neutral software framework to bring together heterogeneous value-add — both commercial and open-source — around a common center of gravity.

Think of EdgeX doing for IoT what Android did for mobile. Read the next posts in this series for considerations when developing edge computing solutions and my related three rules for IoT and edge scale, which are centered on us all being better off focusing on creating value and less reinvention in the middle.

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