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Continuous Request Right-Sizing

Note: This feature is in a pre-release (alpha/beta) state and has limitations. Please read the documentation carefully.
Kubecost's Kubescaler implements continuous request right-sizing: the automatic application of Kubecost's high-fidelity recommendations to your containers' resource requests. This provides an easy way to automatically improve your allocation of cluster resources by improving efficiency.
Kubescaler can be enabled and configured on a per-workload basis so that only the workloads you want edited will be edited.

Setup

See the high-level automatic request right-sizing guide. Kubescaler is part of Cluster Controller.

Usage

Kubescaler is configured on a workload-by-workload basis via annotations. Currently, only deployment workloads are supported.
Annotation
Description
Example(s)
request.autoscaling.kubecost.com/enabled
Whether to autoscale the workload. See note on KUBESCALER_RESIZE_ALL_DEFAULT.
true, false
request.autoscaling.kubecost.com/frequencyMinutes
How often to autoscale the workload, in minutes. If unset, a conservative default is used.
73
request.autoscaling.kubecost.com/scheduleStart
Optional augmentation to the frequency parameter. If both are set, the workload will be resized on the scheduled frequency, aligned to the start. If frequency is 24h and the start is midnight, the workload will be rescheduled at (about) midnight every day. Formatted as RFC3339.
2022-11-28T00:00:00Z
cpu.request.autoscaling.kubecost.com/targetUtilization
Target utilization (CPU) for the recommendation algorithm. If unset, the backing recommendation service's default is used.
0.8
memory.request.autoscaling.kubecost.com/targetUtilization
Target utilization (Memory/RAM) for the recommendation algorithm. If unset, the backing recommendation service's default is used.
0.8
Notable Helm values:
Helm value
Description
Example(s)
clusterController.kubescaler.resizeAllDefault
If true, Kubescaler will switch to default-enabled for all workloads unless they are annotated with request.autoscaling.kubecost.com/enabled=false. This is recommended for low-stakes clusters where you want to prioritize workload efficiency without reworking deployment specs for all workloads.
true

Supported workload types

Kubescaler supports:
  • Deployments
Kubescaler cannot support:
  • "Uncontrolled" Pods. Learn more here.

Example

export NS="kubecost"
export DEP="kubecost-cost-analyzer"
export AN_ENABLE="request.autoscaling.kubecost.com/enabled=true"
export AN_FREQ="request.autoscaling.kubecost.com/frequencyMinutes=660"
export AN_TCPU="cpu.request.autoscaling.kubecost.com/targetUtilization=0.9"
export AN_TMEM="memory.request.autoscaling.kubecost.com/targetUtilization=0.9"
kubectl annotate -n "${NS}" deployment "${DEP}" "${AN_ENABLE}"
kubectl annotate -n "${NS}" deployment "${DEP}" "${AN_FREQ}"
kubectl annotate -n "${NS}" deployment "${DEP}" "${AN_TCPU}"
kubectl annotate -n "${NS}" deployment "${DEP}" "${AN_TMEM}"
Kubescaler will take care of the rest. It will apply the best-available recommended requests to the annotated controller every 11 hours.
To check current requests for your Deployments, use the following command:
kubectl get deployment -n "kubecost" -o=jsonpath="{range .items[*]}"deployment/"{.metadata.name}{'\n'}{range .spec.template.spec.containers[*]}{.name}{'\t'}{.resources.requests}{'\n'}{end}{'\n'}{end}"