Edgecase 2022: Kubernetes at the Edge

Finally, after the pandemic abruptly changed every meetup and conference into a video conference, in-person events are possible again! I attended Edgecase 2022, organized by Fullstaq, and focused on running Kubernetes at the edge. These are my main takeaways.

At Nu.nl, we don’t run Kubernetes at the edge, nor do we intend to. We use AWS. Nevertheless, underlying techniques might still be applicable. Furthermore, it never hurts to look at things from a different perspective, and it’s great to interact with peers that are equally enthusiastic about the technology they use.

  • Low compute/memory specs
  • Need to avoid having to transfer large amounts of data generated at the edge


ArgoCD is a well-established name in the Kubernetes ecosystem. It’s a tool that enables managing applications via GitOps, of which I heard quite some positive things.

  • Similarly, ArgoCD continuously applies the manifests in the cluster following what is in Git.
  • Granting a CI/CD pipeline read/write access to the target environment is arguably harder than providing a system read-only access to a Git repository.
  • Scalability. An increasing amount of clusters or applications does not result in a similar increase in the number of deployment pipelines.


Getting a lot of attention on EdgeCase was K3S, a lightweight Kubernetes distribution. It’s certified (meaning: fully compatible with its big brother Kubernetes) yet more straightforward, packing all of the moving parts of Kubernetes in a single binary.

Lightweight Kubernetes. Easy to install, half the memory, all in a binary of less than 100 MB. Great for: 
* Edge
* IoT * CI
* Development
* Embedding K8s
* Situations where a PhD in K8s clusterology is infeasible


Leafcloud focuses on running compute in a more energy-efficient way. They do so by running compute in ‘Leaf sites’, where heat generated by the servers can be used to replace fossil fuels. Data is stored in a more traditional data center for security reasons, and glass fiber lines connect data to the leaf sites.

  • Turn off things we don’t use
  • Use efficient programming languages


One of the takeaways is that the ‘pets vs. cattle’ paradigm applies to Kubernetes clusters similarly to virtual machines: operating one or many clusters, short- or long-lived, should hardly make a difference. Another is the platform’s versatility: same API, totally different environments.



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