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MR adds new performance test jobs with TopologyAwareScheduling:

  • pull-kueue-test-scheduling-perf-with-tas-main
  • pull-kueue-test-scheduling-perf-with-tas-release-0-15
  • pull-kueue-test-scheduling-perf-with-tas-release-0-16

Don't merge before kubernetes-sigs/kueue#8917 is merged (and cherry-picked to 0.15, 0.16).

Add new performance tests with TopologyAwareScheduling:
- pull-kueue-test-scheduling-perf-with-tas-main
- pull-kueue-test-scheduling-perf-with-tas-release-0-15
- pull-kueue-test-scheduling-perf-with-tas-release-0-16
@k8s-ci-robot k8s-ci-robot added cncf-cla: yes Indicates the PR's author has signed the CNCF CLA. area/config Issues or PRs related to code in /config area/jobs labels Feb 4, 2026
@k8s-ci-robot k8s-ci-robot requested a review from kannon92 February 4, 2026 00:08
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Welcome @ASverdlov!

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@k8s-ci-robot k8s-ci-robot requested a review from mimowo February 4, 2026 00:08
@k8s-ci-robot k8s-ci-robot added sig/testing Categorizes an issue or PR as relevant to SIG Testing. needs-ok-to-test Indicates a PR that requires an org member to verify it is safe to test. size/M Denotes a PR that changes 30-99 lines, ignoring generated files. labels Feb 4, 2026
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Hi @ASverdlov. Thanks for your PR.

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[APPROVALNOTIFIER] This PR is NOT APPROVED

This pull-request has been approved by: ASverdlov
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limits:
cpu: "6"
memory: "9Gi"
- name: pull-kueue-test-scheduling-perf-with-tas-main
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@mimowo @ASverdlov

I assume we'll want to establish a proper baseline using the first couple of runs of this job, so I'm wondering whether we'll break our CI if the initial thresholds in kubernetes-sigs/kueue#8917 are misconfigured.

Should we temporarily make this job optional or maybe make the thresholds very lenient initially?

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That is a valid concern, merging a PR which fails on every second build will not be good. The strategy we took with the baseline tests was:

  1. we run the CI a couple of times on Github to determine the mean and std dev, rather than from local machine
  2. we set the thresholds rather conservative, we want to detect bigger regressions, not 1% slow down due to a new feature. I assume the same level of tolerance should be used, like 20% or so from the mean.

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4 participants