Tweak Horizontal Pod Autoscaler documentation (#10596)

* Use consistent terms

* Fix typo
This commit is contained in:
nownabe
2018-10-12 22:21:18 +09:00
committed by k8s-ci-robot
parent 9936ea6552
commit 753f57f0e6
@@ -141,7 +141,7 @@ the magnitude of any potential scale.
Furthermore, if any not-yet-ready pods were present, and we would have Furthermore, if any not-yet-ready pods were present, and we would have
scaled up without factoring in missing metrics or not-yet-ready pods, we scaled up without factoring in missing metrics or not-yet-ready pods, we
conservatively assume the non-yet-ready pods are consuming 0% of the conservatively assume the not-yet-ready pods are consuming 0% of the
desired metric, further dampening the magnitude of a scale up. desired metric, further dampening the magnitude of a scale up.
After factoring in the not-yet-ready pods and missing metrics, we After factoring in the not-yet-ready pods and missing metrics, we
@@ -163,7 +163,7 @@ from the metrics APIs), scaling is skipped.
Finally, just before HPA scales the target, the scale reccomendation is recorded. The Finally, just before HPA scales the target, the scale reccomendation is recorded. The
controller considers all recommendations within a configurable window choosing the controller considers all recommendations within a configurable window choosing the
highest recommendation from within that window. This value can be configured using the `--horizontal-pod-autoscaler-downscale-stabilization-window` flag, which defaults to 5 minutes. highest recommendation from within that window. This value can be configured using the `--horizontal-pod-autoscaler-downscale-stabilization-window` flag, which defaults to 5 minutes.
This means that scaledowns will occur gradually, smothing out the impact of rapidly This means that scaledowns will occur gradually, smoothing out the impact of rapidly
fluctuating metric values. fluctuating metric values.
## API Object ## API Object