KubeWeekly #123

Published: February 8, 2018

Kubernetes

Highlights From the Links

  1. The Full-Time Job of Keeping up With Kubernetes, Gravitational (https://gravitational.com/blog/kubernetes-release-cycle/)
    If you’ve ever wonder what goes into maintaining an open-source project with as much velocity as Kubernetes, this article is a great place to start. Abraham Ingersoll (https://twitter.com/aberoham) from Gravitational dives into the inner workings of SIGs and working groups, the Kubernetes release cycle, feature development process, and community support.
  2. When to use Serverless? When to use Kubernetes?, Heidloff.Net (http://heidloff.net/article/when-to-use-serverless-kubernetes)
    As the cloud native landscapes evolve, more and more questions come up as to when to use Kubernetes and when to use Serverless to build an application. In this article, Niklas Heidloff (https://twitter.com/nheidloff) from IBM outlines the pros and cons of each option and how to decide what’s best for your use case.
  3. Scheduling in Kubernetes, Alexandrutopliceanu.Ro (http://alexandrutopliceanu.ro/post/scheduling-in-kubernetes/)
    Scheduling in Kubernetes helps ensure that pods are only placed on nodes that have sufficient free resources. In this post, Alexandru Topliceanu (https://twitter.com/topliceanu) from Pusher.com walks you through the implementation of the default scheduler in Kubernetes. Dive into the genericScheduler, volumes, algorithm, predicates, custom scheduler, and more to learn how to support long-running processes.
  4. Kubernetes Autoscaling Based on Custom Metrics Without Using a Host Port, Medium (https://medium.com/@marko.luksa/kubernetes-autoscaling-based-on-custom-metrics-without-using-a-host-port-b783ed6241ac)
    Autoscaling is one of the most useful features in Kubernetes, but autoscaling based on custom metrics can be complicated to set up since it’s still an alpha feature. In this Medium post, Marko Lukša (https://twitter.com/markoluksa) from Red Hat shows you how to set up horizontal pod autoscaling based on application-provided custom metrics on minikube.

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