Refactor ClusterCIDR for internal configuration of kube-proxy
adhering to the v1alpha2 version specifications as detailed in
https://kep.k8s.io/784.
Signed-off-by: Daman Arora <aroradaman@gmail.com>
Consolidate SyncPeriod and MinSyncPeriod for internal configuration
of kube-proxy adhering to the v1alpha2 version specifications as
detailed in https://kep.k8s.io/784.
Signed-off-by: Daman Arora <aroradaman@gmail.com>
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>
The resource claim controller is completely agnostic to the claim spec. It
doesn't care about classes or devices, therefore it needs no changes in 1.31
besides the v1alpha2 -> v1alpha3 renaming from a previous commit.
The advantages of using a validation admission policy (VAP) are that no changes
are needed in Kubernetes and that admins have full flexibility if and how they
want to control which users are allowed to use "admin access" in their
requests.
The downside is that without admins taking actions, the feature is enabled
out-of-the-box in a cluster. Documentation for DRA will have to make it very
clear that something needs to be done in multi-tenant clusters.
The test/e2e/testing-manifests/dra/admin-access-policy.yaml shows how to do
this. The corresponding E2E tests ensures that it actually works as intended.
For some reason, adding the namespace to the message expression leads to a
type check errors, so it's currently commented out.
This adds the ability to select specific requests inside a claim for a
container.
NodePrepareResources is always called, even if the claim is not used by any
container. This could be useful for drivers where that call has some effect
other than injecting CDI device IDs into containers. It also ensures that
drivers can validate configs.
The pod resource API can no longer report a class for each claim because there
is no such 1:1 relationship anymore. Instead, that API reports claim,
API devices (with driver/pool/device as ID) and CDI device IDs. The kubelet
itself doesn't extract that information from the claim. Instead, it relies on
drivers to report this information when the claim gets prepared. This isolates
the kubelet from API changes.
Because of a faulty E2E test, kubelet was told to contact the wrong driver for
a claim. This was not visible in the kubelet log output. Now changes to the
claim info cache are getting logged. While at it, naming of variables and some
existing log output gets harmonized.
Co-authored-by: Oksana Baranova <oksana.baranova@intel.com>
Co-authored-by: Ed Bartosh <eduard.bartosh@intel.com>
Publishing ResourceSlices now supports network-attached devices and the new
v1alpha3 API. The logic for splitting up across different slices is missing.
This is a complete revamp of the original API. Some of the key
differences:
- refocused on structured parameters and allocating devices
- support for constraints across devices
- support for allocating "all" or a fixed amount
of similar devices in a single request
- no class for ResourceClaims, instead individual
device requests are associated with a mandatory
DeviceClass
For the sake of simplicity, optional basic types (ints, strings) where the null
value is the default are represented as values in the API types. This makes Go
code simpler because it doesn't have to check for nil (consumers) and values
can be set directly (producers). The effect is that in protobuf, these fields
always get encoded because `opt` only has an effect for pointers.
The roundtrip test data for v1.29.0 and v1.30.0 changes because of the new
"request" field. This is considered acceptable because the entire `claims`
field in the pod spec is still alpha.
The implementation is complete enough to bring up the apiserver.
Adapting other components follows.
This test is flaky. I have noticed that this happens because the pod is not READY when it is being deleted at the end of the test. This fix ensures that the pod is READY before continuing with the rest of the test.
