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fix discription about horizontal pod autoscale (#10557)

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CaoShuFeng
2018-10-09 19:42:09 +08:00
committed by k8s-ci-robot
parent 8f6c9e6f81
commit cf73622b61
1 changed files with 3 additions and 3 deletions
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content/en/docs/tasks/run-application/horizontal-pod-autoscale.md
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@@ -122,7 +122,7 @@ became ready, that pod is set aside as well.
Due to technical constraints, the HorizontalPodAutoscaler controller Due to technical constraints, the HorizontalPodAutoscaler controller
cannot exactly determine the first time a pod becomes ready when cannot exactly determine the first time a pod becomes ready when
determinining whether to set aside certain CPU metrics. Instead, it determining whether to set aside certain CPU metrics. Instead, it
considers a Pod "not yet ready" if it's unready and transitioned to considers a Pod "not yet ready" if it's unready and transitioned to
unready within a short, configurable window of time since it started. unready within a short, configurable window of time since it started.
This value is configured with the `--horizontal-pod-autoscaler-initial-readiness-delay` flag, and its default is 30 This value is configured with the `--horizontal-pod-autoscaler-initial-readiness-delay` flag, and its default is 30
@@ -139,7 +139,7 @@ conservatively, assuming those pods were consuming 100% of the desired
value in case of a scale down, and 0% in case of a scale up. This dampens value in case of a scale down, and 0% in case of a scale up. This dampens
the magnitude of any potential scale. the magnitude of any potential scale.
Futhermore, 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 non-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.
@@ -161,7 +161,7 @@ into a desired replica count (e.g. due to an error fetching the metrics
from the metrics APIs), scaling is skipped. 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 reccomendations 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, smothing out the impact of rapidly
fluctuating metric values. fluctuating metric values.