class mapping
- Add a new interface "DeviceClassResolver" in the scheduler framework
- Add a global cache of mapping between the extended resource and the
device class
- Cache can be leveraged by the k8s api-server, controller-manager along with the scheduler
- This change helps in delegating the requests to the dynamicresource
plugin based on the mapping during the node update events and thus
avoiding an extra scheduling cycle
Signed-off-by: Sai Ramesh Vanka <svanka@redhat.com>
Previously, the scheduler assumed an extended resource was maintained
by a device plugin if its name was present in the node's Allocatable
map, even if its value was zero. This blocked scheduling when a device
plugin was disconnected or uninstalled, because Kubelet still reported
the resource with Allocatable=0.
This change adds a check for the actual allocatable value in addition
to a key presence check, allowing nodes with uninstalled device
plugins to be considered for scheduling.
* Move ClusterEvent type to staging repo, leaving some functions (that contain logic internal to scheduler) in kubernetes/kubernetes
apply review comment and fix linter warning
* update-vendor.sh
* update doc comments
* run update-vendor.sh
Currently, the NodeResourcesFit plugin always returns Unschedulable when a pod's
resource requests exceed a node's available resources. However, when a pod's
requests exceed the node's total allocatable, preemption cannot help since even
an empty node would not have enough resources.
This change modifies the NodeResourcesFit plugin to return UnschedulableAndUnresolvable
when a pod's resource requests exceed the node's total allocatable. This helps
optimize the scheduling process in large clusters by:
1. Reducing the number of candidate nodes that need to be considered for preemption
2. Providing clearer feedback about unresolvable resource constraints
3. Improving scheduling performance by avoiding unnecessary preemption calculations
The change is particularly beneficial in heterogeneous clusters where node sizes
vary significantly, as it helps quickly identify nodes that are fundamentally
too small for certain pods.
Fixes https://github.com/kubernetes/kubernetes/issues/131310
Co-authored-by: Kensei Nakada <handbomusic@gmail.com>
- Refactored `PreScore` method in `balanced_allocation.go` to skip
best-effort pods.
- Updated unit tests in `balanced_allocation_test.go` to check for
the new status codes.
1. Use pod-level resource when feature is enabled and resources are set at pod-level
2. Edge case handling: When a pod defines only CPU or memory limits at pod-level (but not both), and container-level requests/limits are unset, the pod-level requests stay empty for the resource without a pod-limit. The container's request for that resource is then set to the default request value from schedutil.
