Automatic merge from submit-queue (batch tested with PRs 39807, 37505, 39844, 39525, 39109)
Made cache.Controller to be interface.
**What this PR does / why we need it**:
#37504
In certain conditions in which the set of metrics returned by Heapster
is completely disjoint from the set of pods returned by the API server,
we can have a request sum of zero, which can cause a panic (due to
division by zero). This checks for that condition.
Fixes#39680
Automatic merge from submit-queue (batch tested with PRs 38154, 38502)
Rename "release_1_5" clientset to just "clientset"
We used to keep multiple releases in the main repo. Now that [client-go](https://github.com/kubernetes/client-go) does the versioning, there is no need to keep releases in the main repo. This PR renames the "release_1_5" clientset to just "clientset", clientset development will be done in this directory.
@kubernetes/sig-api-machinery @deads2k
```release-note
The main repository does not keep multiple releases of clientsets anymore. Please find previous releases at https://github.com/kubernetes/client-go
```
Automatic merge from submit-queue
Curating Owners: pkg/controller
cc @jsafrane @mikedanese @bprashanth @derekwaynecarr @thockin @saad-ali
In an effort to expand the existing pool of reviewers and establish a
two-tiered review process (first someone **lgtms** and then someone
experienced in the project **approves**), we are adding new reviewers to
existing owners files.
## If You Care About the Process:
We did this by algorithmically figuring out who’s contributed code to
the project and in what directories. Unfortunately, that doesn’t work
perfectly: people that have made mechanical code changes (e.g change the
copyright header across all directories) end up as reviewers in lots of
places.
Instead of using pure commit data, we generated an excessively large
list of reviewers and pruned based on all time commit data, recent
commit data and review data (number of PRs commented on).
At this point we have a decent list of reviewers, but it needs one last
pass for fine tuning.
## TLDR:
As an owner of a sig/directory and a leader of the project, here’s what
we need from you:
1. Use PR https://github.com/kubernetes/kubernetes/pull/35715 as an example.
2. The pull-request is made editable, please edit the OWNERS file to add
the names of people that should be reviewing code in the future in the **reviewers** section. You probably do NOT need to modify the **approvers** section.
3. Notify me if you want some OWNERS file to be removed. Being an approver or reviewer
of a parent directory makes you a reviewer/approver of the subdirectories too, so not all
OWNERS files may be necessary.
4. Please use ALIAS if you want to use the same list of people over and
over again (don't hesitate to ask me for help, or use the pull-request
above as an example)
Automatic merge from submit-queue
rescale immediately if the basic constraints are not satisfied
refactor reconcileAutoscaler.
If the basic constraints are not satisfied, we should rescale the target ref immediately.
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.
