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.
All logic related to obtaining DRA objects and tracking modifications
to ResourceClaims in-memory is extracted to DefaultDRAManager, which
implements framework.SharedDRAManager.
This is intended to be a no-op in terms of the DRA plugin behavior.
A better place is the cel package because a) the name can become shorter
and b) it is tightly coupled with the compiler there.
Moving the compilation into the cache simplifies the callers.
"Allocated devices" are the ones which can be observed from the informer. "All
allocated devices" also includes those which are in flight and haven't been
written back to the apiserver.
The logic for skipping "admin access" was repeated in three different places. A
single foreachAllocatedDevices with a callback puts it into one function.
DeviceClasses and different requests are very likely to contain the same
expression string. We don't need to compile that over and over again.
To avoid hanging onto that cache longer than necessary, it's currently tied to
each PreFilter/Filter combination. It might make sense to move this up into the
scheduler plugin and thus reuse compiled expressions for different pods.
goos: linux
goarch: amd64
pkg: k8s.io/kubernetes/test/integration/scheduler_perf
cpu: Intel(R) Core(TM) i9-7980XE CPU @ 2.60GHz
│ before │ after │
│ SchedulingThroughput/Average │ SchedulingThroughput/Average vs base │
PerfScheduling/SchedulingWithResourceClaimTemplateStructured/5000pods_500nodes-36 33.95 ± 4% 36.65 ± 2% +7.95% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/empty_100nodes-36 105.8 ± 2% 106.7 ± 3% ~ (p=0.177 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/empty_500nodes-36 100.7 ± 1% 119.7 ± 3% +18.82% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/half_100nodes-36 90.78 ± 1% 121.10 ± 4% +33.40% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/half_500nodes-36 50.51 ± 7% 63.72 ± 3% +26.17% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/full_100nodes-36 103.7 ± 5% 110.2 ± 2% +6.32% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/full_500nodes-36 28.50 ± 2% 28.16 ± 5% ~ (p=0.102 n=6)
geomean 64.99 73.15 +12.56%
Using unique strings instead of normal strings speeds up allocation with
structured parameters because maps that use those strings as key no longer need
to build hashes of the string content. However, care must be taken to call
unique.Make as little as possible because it is costly.
Pre-allocating the map of allocated devices reduces the need to grow the map
when adding devices.
goos: linux
goarch: amd64
pkg: k8s.io/kubernetes/test/integration/scheduler_perf
cpu: Intel(R) Core(TM) i9-7980XE CPU @ 2.60GHz
│ before │ after │
│ SchedulingThroughput/Average │ SchedulingThroughput/Average vs base │
PerfScheduling/SchedulingWithResourceClaimTemplateStructured/5000pods_500nodes-36 18.06 ± 2% 33.30 ± 2% +84.31% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/empty_100nodes-36 104.7 ± 2% 105.3 ± 2% ~ (p=0.818 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/empty_500nodes-36 96.62 ± 1% 100.75 ± 1% +4.28% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/half_100nodes-36 83.00 ± 2% 90.96 ± 2% +9.59% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/half_500nodes-36 32.45 ± 7% 49.84 ± 4% +53.60% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/full_100nodes-36 95.22 ± 7% 103.80 ± 1% +9.00% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/full_500nodes-36 9.111 ± 10% 27.215 ± 7% +198.69% (p=0.002 n=6)
geomean 45.86 64.26 +40.12%
The Allocate call used to call back into the claim lister for each node. This
was significant work which showed up at the top of the CPU profile. It's
okay to list only once during PreFilter because the Filter call does not change
the claim status between Allocate calls.
goos: linux
goarch: amd64
pkg: k8s.io/kubernetes/test/integration/scheduler_perf
cpu: Intel(R) Core(TM) i9-7980XE CPU @ 2.60GHz
│ before │ after │
│ SchedulingThroughput/Average │ SchedulingThroughput/Average vs base │
PerfScheduling/SchedulingWithResourceClaimTemplateStructured/5000pods_500nodes-36 15.04 ± 0% 18.06 ± 2% +20.07% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/empty_100nodes-36 105.5 ± 1% 104.7 ± 2% ~ (p=0.485 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/empty_500nodes-36 95.83 ± 1% 96.62 ± 1% ~ (p=0.063 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/half_100nodes-36 79.67 ± 3% 83.00 ± 2% +4.18% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/half_500nodes-36 27.11 ± 5% 32.45 ± 7% +19.68% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/full_100nodes-36 84.00 ± 3% 95.22 ± 7% +13.36% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/full_500nodes-36 7.110 ± 6% 9.111 ± 10% +28.15% (p=0.002 n=6)
geomean 41.05 45.86 +11.73%
Introducing pdb to preemption had disrupted the orderliness of pods in the victims,
which would leads picking wrong victim node with higher priority pod on it.
