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Update HPA Algorithm Docs for v1.15 (#14728)

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Also changes a link to algorithm details from initial design proposal in Github.
This commit is contained in:
Guy Templeton
2019-06-11 03:04:17 +01:00
committed by Kubernetes Prow Robot
parent 2a0f39fb41
commit d9b19701c3
2 changed files with 6 additions and 3 deletions
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content/en/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough.md
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@@ -81,7 +81,7 @@ controlled by the php-apache deployment we created in the first step of these in
Roughly speaking, HPA will increase and decrease the number of replicas Roughly speaking, HPA will increase and decrease the number of replicas
(via the deployment) to maintain an average CPU utilization across all Pods of 50% (via the deployment) to maintain an average CPU utilization across all Pods of 50%
(since each pod requests 200 milli-cores by [kubectl run](https://github.com/kubernetes/kubernetes/blob/{{< param "githubbranch" >}}/docs/user-guide/kubectl/kubectl_run.md), this means average CPU usage of 100 milli-cores). (since each pod requests 200 milli-cores by [kubectl run](https://github.com/kubernetes/kubernetes/blob/{{< param "githubbranch" >}}/docs/user-guide/kubectl/kubectl_run.md), this means average CPU usage of 100 milli-cores).
See [here](https://git.k8s.io/community/contributors/design-proposals/autoscaling/horizontal-pod-autoscaler.md#autoscaling-algorithm) for more details on the algorithm. See [here](/docs/tasks/run-application/horizontal-pod-autoscale/#algorithm-details) for more details on the algorithm.
```shell ```shell
kubectl autoscale deployment php-apache --cpu-percent=50 --min=1 --max=10 kubectl autoscale deployment php-apache --cpu-percent=50 --min=1 --max=10
content/en/docs/tasks/run-application/horizontal-pod-autoscale.md
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@@ -155,9 +155,12 @@ used.
If multiple metrics are specified in a HorizontalPodAutoscaler, this If multiple metrics are specified in a HorizontalPodAutoscaler, this
calculation is done for each metric, and then the largest of the desired calculation is done for each metric, and then the largest of the desired
replica counts is chosen. If any of those metrics cannot be converted replica counts is chosen. If any of these metrics cannot be converted
into a desired replica count (e.g. due to an error fetching the metrics 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) and a scale down is suggested by the metrics which
can be fetched, scaling is skipped. This means that the HPA is still capable
of scaling up if one or more metrics give a `desiredReplicas` greater than
the current value.
Finally, just before HPA scales the target, the scale recommendation is recorded. The Finally, just before HPA scales the target, the scale recommendation is recorded. The
controller considers all recommendations within a configurable window choosing the controller considers all recommendations within a configurable window choosing the