Some shared services need more copies as a Kubernetes cluster grows, even if you do not scale them from their own CPU usage. Cluster Proportional Autoscaler counts schedulable nodes or processor cores and adjusts a target workload using rules you configure. DNS is one possible use when its demand follows cluster size. This is a size-based rule, so it will not necessarily respond to a traffic spike on an otherwise unchanged cluster.
Deployment and operating notes
The current Kubernetes SIG repository describes a controller that watches schedulable node and core counts and changes a target’s replica count using configured scaling parameters. That remains a distinct model from a Horizontal Pod Autoscaler, which uses metrics. The recovered statement that HPA requires Heapster is obsolete and should not be used when designing a current cluster.
Cluster-size scaling can suit infrastructure such as DNS when capacity demand broadly follows node count. Confirm that relationship with measurements before choosing a linear or ladder configuration. Set sensible replica bounds and identify exactly which workload the controller may scale. Avoid two controllers writing the same replica field, including a managed DNS autoscaler supplied by the cluster provider. Test node addition and removal, then confirm target availability and request latency at the resulting replica counts. Preserve the previous scaling ConfigMap and a manual replica target for rollback. Proportional replicas do not automatically protect against sudden request spikes unrelated to cluster size.
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This container image watches over the number of schedule-able nodes and cores of the cluster and resizes the number of replicas for the required resource. This functionality may be desirable for applications that need to be autoscaled with the size of the cluster, such as DNS and other services that scale with the number of nodes/pods in the cluster.
Comparisons to the Horizontal Pod Autoscaler feature
The Horizontal Pod Autoscaler is a top-level Kubernetes API resource. It is a closed feedback loop autoscaler which monitors CPU utilization of the pods and scales the number of replicas automatically. It requires the CPU resources to be defined for all containers in the target pods and also requires heapster to be running to provide CPU utilization metrics.
This horizontal cluster proportional autoscaler is a DIY container (because it is not a Kubernetes API resource) that provides a simple control loop that watches the cluster size and scales the target controller. The actual CPU or memory utilization of the target controller pods is not an input to the control loop, the sole inputs are number of schedulable cores and nodes in the cluster. There is no requirement to run heapster and/or provide CPU resource limits as in HPAs.
The ConfigMap provides the operator with the ability to tune the replica scaling explicitly.
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