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Borg took that further, perhaps due to its workqueue heritage. Babysitter had a machine assigner. Like Borg, the "scheduler" in Kubernetes principally does placement.
"Scheduler" in the context of systems like Kubernetes, Mesos, and Peloton has apparently now come to imply the entire computational resource management ecosystem, including queuing, coscheduling, quota and sharing management, autoscaling, oversubscription, placement, etc
And preemption, descheduling, and so on. It's important to understand how all these control loops interact with each other and with their surroundings, such as load balancers and infrastructure autoprovisioners. But in Kubernetes we've tried to make these functions composable
As I mentioned in my podcast, the importance of some of these is greatly dependent on the variety of workloads run, the elasticity of the underlying infrastructure, and priorities. For instance, many use Kubernetes to accelerate deployment more than to increase utilization
If the infrastructure is not very elastic, as in bare metal datacenter environments, then utilization can be a primary concern, and that's where techniques such as vertical scaling, oversubscription, binpacking, mixing workloads, QoS, share management, etc. become important
VMs in cloud is just one way to make the infrastructure more elastic. It does introduce a 2nd level of placement with information hiding, though, which is a tradeoff. I'll plug the Omega paper for more on that topic:https://ai.google/research/pubs/pub41684 …
I'll come back to scheduling later in the series to talk about scheduling affinity, anti-affinity, taints, tolerations, forgiveness, and so on, which were straight out of our work on Omega.
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