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Hi Gino, marking this as not under considering for this year for now, as we will need more information from you on this before it can be considered. Thanks
hi @Gino
This is an interesting idea, and could be applicable only to those k8s workload types that would support scaling down replicas (for example daemonsets and mirror pods are not technically feasible here, and would be handle with node deletion).
We need to build out some requirements here, because there is more to this.
The motivation around managing VPC https://www.ibm.com/docs/en/license-metric-tool?topic=metrics-virtual-processor-core-vpc is good but what kinds of licenses do we want to impact here? CPU Limit or Node VPC? both?
Since Turbonomic considers every workload (pod or vm) at the same level of priority, would this user define a group of workload that they consider "not needed"?
What KPIs would warrant spinning down? Would the user expect to set this as a "threshold" and apply a schedule?
Would the user also automate pods moves so that we could delete a node, which may be the only way to impact the VPC license?
How does the user want to handle spinning this back up? By schedule or expect the user to trigger scaling back out the replicas on their own? What if another node is needed - how would they handle pod pending?
There is a scenario to consider: If the license is allocated to the nodes, then shutting down pods may not help. if you pin pods to nodes (affinity/anti-affinity, node label, taint) and not allow freedom to move the pods that are already running, we may not be able consolidate and suspend nodes if there is even one app pod holding on to that node. Neither could kubernetes suspend that node because the pod running there would not have a compliant node to be scheduled on if the constraints are very restrictive. Turbo can move workloads around but we must respect constraints. Would like your thoughts on how to communicate this because suspending pods would not be enough.