[Orch] Add orch k8s to rc state

[JLineaweaver]

What does this PR do?

• Adds the orchestrator k8s crd product to the agent state

Motivation

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

[pr-commenter[bot]]

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv create-vm --pipeline-id=44514200 --os-family=ubuntu

Note: This applies to commit 44560c2c

[pr-commenter[bot]]

Regression Detector

Regression Detector Results

Run ID: 9e346d86-d2c3-4ec7-b73a-7d4336b3cd0b Metrics dashboard Target profiles

Baseline: 9f339cb1aaca95f84d756e65b6238bbdae3358c4 Comparison: 44560c2c391ceba5d2af64709d94785b471f45a2

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

No significant changes in experiment optimization goals

Confidence level: 90.00% Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	pycheck_lots_of_tags	% cpu utilization	+1.55	[-1.15, +4.24]	1	Logs
➖	basic_py_check	% cpu utilization	+1.18	[-1.79, +4.16]	1	Logs
➖	otel_to_otel_logs	ingress throughput	+0.81	[-0.02, +1.64]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.73	[-0.04, +1.49]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.01	[-0.02, +0.03]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	idle	memory utilization	-0.16	[-0.20, -0.11]	1	Logs
➖	file_tree	memory utilization	-0.27	[-0.38, -0.16]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-0.53	[-0.59, -0.48]	1	Logs

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
✅	idle	memory_usage	10/10

Explanation

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".