The New Stack Podcast

#174: Kubernetes and the Return of the Virtual Machines

Episode Summary

This week on The New Stack Analysts podcast, we take a closer look at the appeal of using virtual machines in Kubernetes environments. The discussion was sparked by a popular blog post penned last month by Pivotal Principal Technologist Paul Czarkowski. The problem with basic Docker-styled containers is that they do not offer sufficient security in multitenant environments, where multiple deployments intermingle on the same set of Kubernetes-controlled servers. So we spoke with Czarkowski to learn more of his thinking. Linux containers all rely on a shared kernel from the kernel, and isolation is provided by the kernel through namespaces. The Kubernetes API, however, is not secured, and most K8s components are not aware of the tenants. This is forcing service providers to provision Kubernetes workloads for different clients as separate clusters, not taking full advantage of the full savings that Kubernetes could provide by pooling workloads on the same cluster, Czarkowski argued.

Episode Notes

This week on The New Stack Analysts podcast, we take a closer look at the appeal of using virtual machines in Kubernetes environments.

The discussion was sparked by a popular blog post penned last month by Pivotal Principal Technologist Paul Czarkowski. The problem with basic Docker-styled containers is that they do not offer sufficient security in multitenant environments, where multiple deployments intermingle on the same set of Kubernetes-controlled servers. So we spoke with Czarkowski to learn more of his thinking.

Linux containers all rely on a shared kernel from the kernel, and isolation is provided by the kernel through namespaces. The Kubernetes API, however, is not secured, and most K8s components are not aware of the tenants. This is forcing service providers to provision Kubernetes workloads for different clients as separate clusters, not taking full advantage of the full savings that Kubernetes could provide by pooling workloads on the same cluster, Czarkowski argued.