Signed-off-by: wangyysde <net_use@bzhy.com>
Generation swagger.json.
Use v2 path for hpa_cpu_field.
run update-codegen.sh
Signed-off-by: wangyysde <net_use@bzhy.com>
The HPA controller keeps a flat history of recommendations for
stabilization. However when both up and down scale stabilization are
configured, the interpretation of the history changes depending on the
direction of movement. What we want is to keep the stabilized
recommendation within the envelope of the minimum and maximum over
configured stabilization windows. We should only move when the
envelope forces a move.
Add support for scaling to zero pods
minReplicas is allowed to be zero
condition is set once
Based on https://github.com/kubernetes/kubernetes/pull/61423
set original valid condition
add scale to/from zero and invalid metric tests
Scaling up from zero pods ignores tolerance
validate metrics when minReplicas is 0
Document HPA behaviour when minReplicas is 0
Documented minReplicas field in autoscaling APIs
current scale. Two important ones are when missing metrics might
change the direction of scaling, and when the recommended scale is
within tolerance of the current scale.
The way that ReplicaCalculator signals it's desire to not change the
current scale is by returning the current scale. However the current
scale is from scale.Status.Replicas and can be larger than
scale.Spec.Replicas (e.g. during Deployment rollout with configured
surge). This causes a positive feedback loop because
scale.Status.Replicas is written back into scale.Spec.Replicas,
further increasing the current scale.
This PR fixes the feedback loop by plumbing the replica count from
spec through horizontal.go and replica_calculator.go so the calculator
can punt with the right value.
Handle a case in the Horizontal Pod Autoscaler Controller when scaling
on multiple metrics and one or more is missing or invalid.
If all metrics are missing - return an error and leave the isScalingActive
condition as that for the last invalid metric.
If some metrics are missing/invalid and some are valid and found -
if a scale up would be triggered by the valid metrics ignore the missing
metrics and scale up, if a scale down would be triggered, return an error
and leave the isScalingActive condition as that for the last invalid metric.
- Move from the old github.com/golang/glog to k8s.io/klog
- klog as explicit InitFlags() so we add them as necessary
- we update the other repositories that we vendor that made a similar
change from glog to klog
* github.com/kubernetes/repo-infra
* k8s.io/gengo/
* k8s.io/kube-openapi/
* github.com/google/cadvisor
- Entirely remove all references to glog
- Fix some tests by explicit InitFlags in their init() methods
Change-Id: I92db545ff36fcec83afe98f550c9e630098b3135
HPA will treat initial size of autoscalee to avoid hastily overriding
recomendations made by HPA (if HPA set size and then was restarted) or by user
(initial size should be treated as human-generated recommendation).
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.
