fix(fleet): Fix flaky test

[BaptisteFoy]

What does this PR do?

• Removes a test marked as flaky that isn't
• Try to fix another flaky test by
  • Making sure to grab all systemd events
  • Resetting the failed systemd counter after each job -- that may make the experiment fail right at start
  • Waiting for the exp units to actually be started -- this should change anything but if the experiment failing right up is the right thing we'll know

Motivation

Describe how to test/QA your changes

Possible Drawbacks / Trade-offs

Additional Notes

[agent-platform-auto-pr]

[Fast Unit Tests Report]

On pipeline 46919266 (CI Visibility). The following jobs did not run any unit tests:

Jobs:

• tests_deb-arm64-py3
• tests_deb-x64-py3
• tests_flavor_dogstatsd_deb-x64
• tests_flavor_heroku_deb-x64
• tests_flavor_iot_deb-x64
• tests_rpm-arm64-py3
• tests_rpm-x64-py3
• tests_windows-x64

If you modified Go files and expected unit tests to run in these jobs, please double check the job logs. If you think tests should have been executed reach out to #agent-devx-help

[agent-platform-auto-pr]

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=46919266 --os-family=ubuntu

Note: This applies to commit 0cb3473

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Run ID: 333e7142-ef57-4469-943a-4c9e59cd4201 Metrics dashboard Target profiles

Baseline: 0eab229
Comparison: 0cb3473

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
➖	idle_all_features	memory utilization	+3.29	[+3.17, +3.41]	1	Logs bounds checks dashboard
➖	pycheck_lots_of_tags	% cpu utilization	+1.07	[-1.47, +3.61]	1	Logs
➖	basic_py_check	% cpu utilization	+0.52	[-2.10, +3.14]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.39	[-0.10, +0.87]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.01	[-0.21, +0.24]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.01	[-0.17, +0.19]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.11, +0.11]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.04	[-0.37, +0.30]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.13	[-0.37, +0.12]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.17	[-0.89, +0.55]	1	Logs
➖	otel_to_otel_logs	ingress throughput	-0.27	[-1.07, +0.53]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-1.12	[-1.18, -1.07]	1	Logs
➖	file_tree	memory utilization	-1.32	[-1.45, -1.20]	1	Logs
➖	idle	memory utilization	-1.34	[-1.40, -1.28]	1	Logs bounds checks dashboard

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_300ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	idle	memory_usage	10/10
✅	idle_all_features	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".