Comparing the edge to containers

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This is similar, at least in concept, to other ways in which highly concentrated, centralized IT approaches—say, running a monolithic application in an on-premises data center—are being complemented or replaced by much more distributed approaches, such as running microservices-based applications in containers across multiple clouds.

Just as automation has quickly become critical for managing containerized applications (e.g. configuration management and Kubernetes) — especially across multiple environments — it will play a key role in managing the edge.

3. Standardization and consistency are your friends
The comparison with containerization carries over to a related but independently important thing: standardization.

“The corollary to this requirement is maximum consistency from the data center to the edge,” Huff says. “Deploying and operating large-scale distributed infrastructures is difficult enough without adding randomness and silos.”

In fact, demonstrates a specific potential connection - organizations that already use Kubernetes in their data centers and/or clouds are increasingly using it (albeit perhaps in a lightweight version) at the edge to ensure consistency.

Standardized operating system configurations and cluster orchestration are your friends. For example, organizations using Kubernetes in their data centers are increasingly using leaner versions of Kubernetes as a single-node edge cluster for applications such as telco radio access networks (RAN) or in-vehicle operations.

You don't want to deal with a bunch of one-off ghana mobile database or "snowflakes" in a large-scale edge architecture, do you?

4. Don't forget about monitoring and observability
According to Howell, if you think of the edge as another form of (highly) distributed computing, then you have to understand the need for visibility.

Indeed, monitoring and observability—familiar terms from the cloud universe—join automation and standardization on the edge computing VIP list. You absolutely need the ability to monitor and measure (the latter increasingly referred to in IT as observability) what’s happening in your edge environments.

“Edge computing creates more points of failure — both on the edge computing devices themselves and on the network paths between the edge and the cloud,” Linden says. “And it’s critical to set up monitoring for each point where failure might occur and ensure that by running tests that simulate potential downtime or other issues, the resulting alerts direct your teams to the real point of failure.”