- Added new option to the `InterPodAffinity` scheduler plugin to ignore existing
  pods` preferred inter-pod affinities if the incoming pod has no preferred inter-pod
  affinities. This option can be used as an optimization for higher scheduling throughput
  (at the cost of an occasional pod being scheduled non-optimally/violating existing
  pods preferred inter-pod affinities). To enable this scheduler option, set the

## Deprecations
* Remove extensions/v1beta1 Jobs resource, and job/v1beta1 generator. ([#38614](https://github.com/kubernetes/kubernetes/pull/38614), [@soltysh](https://github.com/soltysh))

- User can now use component config to configure NodeLabel plugin for the scheduler framework. ([#84297](https://github.com/kubernetes/kubernetes/pull/84297), [@liu-cong](https://github.com/liu-cong))
- Optimize inter-pod affinity preferredDuringSchedulingIgnoredDuringExecution type, up to 4x in some cases. ([#84264](https://github.com/kubernetes/kubernetes/pull/84264), [@ahg-g](https://github.com/ahg-g))

- github.com/go-kit/log: [v0.1.0](https://github.com/go-kit/log/tree/v0.1.0)
- github.com/gofrs/uuid: [v4.0.0+incompatible](https://github.com/gofrs/uuid/tree/v4.0.0)
- github.com/josharian/intern: [v1.0.0](https://github.com/josharian/intern/tree/v1.0.0)
- github.com/jpillora/backoff: [v1.0.0](https://github.com/jpillora/backoff/tree/v1.0.0)
- github.com/nxadm/tail: [v1.4.4](https://github.com/nxadm/tail/tree/v1.4.4)

  2. when transitioning to terminal phase for Job, the number of terminating pods is 0
  3. terminal Job conditions (Failed and Complete) should be preceded by adding the corresponding interim conditions: FailureTarget and SuccessCriteriaMet ([#125510](https://github.com/kubernetes/kubernetes/pull/125510), [@mimowo](https://github.com/mimowo)) [SIG Apps and Testing]

- Add improved load balancer naming.

### SIG-scheduling

SIG Scheduling development efforts have been primarily focused on improving performance and reliability of the scheduler.
- Performance of the inter-pod affinity/anti-affinity feature is improved over 100X via algorithmic optimization.