objects.
Change the order of operations to stop current iteration if no changes
to the service chains are needed.
Bump syncProxy frequency to 1 hour.
In a test kind cluster creation of 10K services, 2 endpoints each,
takes ~25m before the fix and ~9min after. Maximum memory usage
during creation is ~650MiB and 260MiB respectively.
Another important metric is the time it takes to create 1 new service
when 10K svc already exist. It used to take ~8m before the fix,
with partialSync it takes ~141ms.
Signed-off-by: Nadia Pinaeva <n.m.pinaeva@gmail.com>
Split running the Wardle aggregated API into preparation and
running phase. This allows reusing the prepared options and
makes it possible for us to introduce additional hooks into
the server authorization flow.
Some of the E2E node tests were flaky. Their timeout apparently was chosen
under the assumption that kubelet would retry immediately after a failed gRPC
call, with a factor of 2 as safety margin. But according to
0449cef8fd,
kubelet has a different, higher retry period of 90 seconds, which was exactly
the test timeout. The test timeout has to be higher than that.
As the tests don't use the gRPC call timeout anymore, it can be made
private. While at it, the name and documentation gets updated.
This fixes the message (node name and "cluster-scoped" were switched) and
simplifies the VAP:
- a single matchCondition short circuits completely unless they're a user
we care about
- variables to extract the userNodeName and objectNodeName once
(using optionals to gracefully turn missing claims and fields into empty strings)
- leaves very tiny concise validations
Co-authored-by: Jordan Liggitt <liggitt@google.com>
In the API, the effect of the feature gate is that alpha fields get dropped on
create. They get preserved during updates if already set. The
PodSchedulingContext registration is *not* restricted by the feature gate.
This enables deleting stale PodSchedulingContext objects after disabling
the feature gate.
The scheduler checks the new feature gate before setting up an informer for
PodSchedulingContext objects and when deciding whether it can schedule a
pod. If any claim depends on a control plane controller, the scheduler bails
out, leading to:
Status: Pending
...
Warning FailedScheduling 73s default-scheduler 0/1 nodes are available: resourceclaim depends on disabled DRAControlPlaneController feature. no new claims to deallocate, preemption: 0/1 nodes are available: 1 Preemption is not helpful for scheduling.
The rest of the changes prepare for testing the new feature separately from
"structured parameters". The goal is to have base "dra" jobs which just enable
and test those, then "classic-dra" jobs which add DRAControlPlaneController.
The structured parameter allocation logic was written from scratch in
staging/src/k8s.io/dynamic-resource-allocation/structured where it might be
useful for out-of-tree components.
Besides the new features (amount, admin access) and API it now supports
backtracking when the initial device selection doesn't lead to a complete
allocation of all claims.
Co-authored-by: Ed Bartosh <eduard.bartosh@intel.com>
Co-authored-by: John Belamaric <jbelamaric@google.com>
