Automatic merge from submit-queue. If you want to cherry-pick this change to another branch, please follow the instructions here: https://github.com/kubernetes/community/blob/master/contributors/devel/cherry-picks.md.
Change CPU sample sanitization in HPA.
**What this PR does / why we need it**:
Change CPU sample sanitization in HPA.
Ignore samples if:
- Pod is beeing initalized - 5 minutes from start defined by flag
- pod is unready
- pod is ready but full window of metric hasn't been colected since
transition
- Pod is initialized - 5 minutes from start defined by flag:
- Pod has never been ready after initial readiness period.
**Release notes:**
```release-note
Improve CPU sample sanitization in HPA by taking metric's freshness into account.
```
Ignore samples if:
- Pod is beeing initalized - 5 minutes from start defined by flag
- pod is unready
- pod is ready but full window of metric hasn't been colected since
transition
- Pod is initialized - 5 minutes from start defined by flag:
- Pod has never been ready after initial readiness period.
Automatic merge from submit-queue (batch tested with PRs 67067, 67947). If you want to cherry-pick this change to another branch, please follow the instructions <a href="https://github.com/kubernetes/community/blob/master/contributors/devel/cherry-picks.md">here</a>.
Do not count soft-deleted pods for scaling purposes in HPA controller
**What this PR does / why we need it**:
The metrics of "soft-deleted" pods in general to be deleted should probably not matter for scaling purposes, since they'll be gone "soon", whether they're nodelost or just normally delete.
As long as soft-deleted pods still exist, they prevent normal scale up.
**Which issue(s) this PR fixes** *(optional, in `fixes #<issue number>(, fixes #<issue_number>, ...)` format, will close the issue(s) when PR gets merged)*:
Fixes https://github.com/kubernetes/kubernetes/issues/62845
**Special notes for your reviewer**:
**Release note**:
```release-note
Stop counting soft-deleted pods for scaling purposes in HPA controller to avoid soft-deleted pods incorrectly affecting scale up replica count calculation.
```
Duration of initialization taint on CPU and window of initial readiness
setting controlled by flags.
Adding API violation exceptions following example of e50340ee23
After my previous changes HPA wasn't behaving correctly in the following
situation:
- Pods use a lot of CPU during initilization, become ready right after they initialize,
- Scale up triggers,
- When new pods become ready HPA counts their usage (even though it's not related to any work that needs doing),
- Another scale up, even though existing pods can handle work, no problem.
Instead discard metric values for pods that are unready and have never
been ready (they may report misleading values, the original reason for
introducing scale up forbidden window).
Use per pod metric when pod is:
- Ready, or
- Not ready but creation timestamp and last readiness change are more
than 10s apart.
In the latter case we asume the pod was ready but later became unready.
We want to use metrics for such pods because sometimes such pods are
unready because they were getting too much load.
Similar to the change we made for `GetObjectMetricReplicas` in the
previous commit. Ensure that `GetExternalMetricReplicas` does not
include unready pods when its determining how many replica it desires.
Including unready pods can lead to over-scaling.
We did not change the behavior of `GetExternalPerPodMetricReplicas`, as
it is slightly less clear what is the desired behavior. We did make some
small naming refactorings to this method, which will make it easier to
ignore unready pods if we decide we want to.
Previously, when `GetObjectMetricReplicas` calculated the desired
replica count, it multiplied the usage ratio by the current number of replicas.
This method caused over-scaling when there were pods that were not ready
for a long period of time. For example, if there were pods A, B, and C,
and only pod A was ready, and the usage ratio was 500%, we would
previously specify 15 pods as the desired replicas (even though really
only one pod was handling the load).
After this change, we now multiple the usage
ratio by the number of ready pods for `GetObjectMetricReplicas`.
In the example above, we'd only desire 5 replica pods.
This change gives `GetObjectMetricReplicas` the same behavior as the
other replica calculator methods. Only `GetExternalMetricReplicas` and
`GetExternalPerPodMetricRepliacs` still allow unready pods to impact the
number of desired replicas. I will fix this issue in the following
commit.
Currently, when performing a scale up, any failed pods (which can be present for example in case of evictions performed by kubelet) will be treated as unready. Unready pods are treated as if they had 0% utilization which will slow down or even block scale up.
After this change, failed pods are ignored in all calculations. This way they do not influence neither scale up nor scale down replica calculations.
Fix#18155
Make HPA tolerance configurable as a flag. This change allows us to use
different tolerance values in production/testing.
Signed-off-by: mattjmcnaughton <mattjmcnaughton@gmail.com>
This commit converts the HPA controller over to using the new version of
the HorizontalPodAutoscaler object found in autoscaling/v2alpha1. Note
that while the autoscaler will accept requests for object metrics, the
scale client will return an error on attempts to get object metrics
(since that requires the new custom metrics API, which is not yet
implemented).
This also enables the HPA object in v2alpha1 as a retrievable API
version by default.
Currently, the HPA considers unready pods the same as ready pods when
looking at their CPU and custom metric usage. However, pods frequently
use extra CPU during initialization, so we want to consider them
separately.
This commit causes the HPA to consider unready pods as having 0 CPU
usage when scaling up, and ignores them when scaling down. If, when
scaling up, factoring the unready pods as having 0 CPU would cause a
downscale instead, we simply choose not to scale. Otherwise, we simply
scale up at the reduced amount caculated by factoring the pods in at
zero CPU usage.
The effect is that unready pods cause the autoscaler to be a bit more
conservative -- large increases in CPU usage can still cause scales,
even with unready pods in the mix, but will not cause the scale factors
to be as large, in anticipation of the new pods later becoming ready and
handling load.
Similarly, if there are pods for which no metrics have been retrieved,
these pods are treated as having 100% of the requested metric when
scaling down, and 0% when scaling up. As above, this cannot change the
direction of the scale.
This commit also changes the HPA to ignore superfluous metrics -- as
long as metrics for all ready pods are present, the HPA we make scaling
decisions. Currently, this only works for CPU. For custom metrics, we
cannot identify which metrics go to which pods if we get superfluous
metrics, so we abort the scale.
Arjun Naik