autoscaler

Yes, though the problems are real. In its current form Kubernetes can only handle a cluster size of 5,000 nodes: https://kubernetes.io/docs/setup/cluster-large/ and high-churn batch workloads don't work well. Kubernetes does support elastic resource sharing (assuming I understand what they need) via horizontal pod scaling and the cluster autoscaler: https://github.com/kubernetes/autoscaler/tree/master/cluster.... Although there are rough edges here, I was surprised by the quality of the code in the cluster autoscaler. I kept thinking of new edge cases and expected it to fall over, only to find out that they'd thought of that problem and it handled it gracefully. For batch workloads something like Argo can help: https://github.com/argoproj/argo. So basically I agree, it probably would've made more sense to build something on top of Kuberne

Autoscaler(https://github.com/kubernetes/autoscaler/tree/master/cluster...) is a good option too. Works incredibly well at automatically adding/removing nodes based on workload.

Running K8S on other environments isn't a difficult job, but you do need a certain level of experience running systems. You need to set up EtcD, in prod you have 3 or 5 of them - there are a couple of topologies for running EtcD and peer discovery (you will need to RTFM). This is all your K8S state, so you need to set up backups for these. Each node you are going to include in your cluster needs to have docker and kubelet running. You will need to start kubelet with your init system (systemd, etc). Then all you need is to bootstrap a control plane. The control plane has 3 components. The API server needs to communicate to etcd, everything else just talks to the API server. The API server is stateless and can (should) have multiple instances running. The other two parts are the Controller and Scheduler. Also stateless. The tool Bootkube[1]