[SMP] Establish experiment naming prefix convention for Quality Gates

[scottopell]

What does this PR do?

Duplicates the two existing quality-gate experiments and prefixes the experiment name with quality_gate_

Motivation

Some SMP experiments are designed to be the most representative of overall Datadog Agent behavior, these will have strict upper bounds and will have been audited in more depth than others.

These "Quality Gate" experiments will be identified by this quality_gate_ prefix.

Describe how to test/QA your changes

n/a

Possible Drawbacks / Trade-offs

Additional Notes

[agent-platform-auto-pr]

[Fast Unit Tests Report]

On pipeline 46927687 (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

[GeorgeHahn]

What do you think about adding a blurb about quality gates and a link to our docs in these?

[scottopell]

Yes, I plan to add this once the corresponding confluence docs exist

[GeorgeHahn]

👀 🙈

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Run ID: 75a9b266-100c-4218-b789-09684ecc77ae Metrics dashboard Target profiles

Baseline: 16c16b2
Comparison: 51f22c1

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
➖	quality_gate_idle_all_features	memory utilization	+1.77	[+1.64, +1.89]	1	Logs bounds checks dashboard
➖	pycheck_lots_of_tags	% cpu utilization	+0.67	[-1.84, +3.18]	1	Logs
➖	file_tree	memory utilization	+0.52	[+0.37, +0.68]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.38	[-0.32, +1.09]	1	Logs
➖	quality_gate_idle	memory utilization	+0.12	[+0.07, +0.16]	1	Logs bounds checks dashboard
➖	file_to_blackhole_300ms_latency	egress throughput	+0.08	[-0.10, +0.26]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.03	[-0.31, +0.36]	1	Logs
➖	basic_py_check	% cpu utilization	+0.01	[-2.71, +2.74]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.01	[-0.21, +0.23]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.01	[-0.10, +0.11]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-0.12	[-0.18, -0.07]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.19	[-0.43, +0.06]	1	Logs
➖	idle_all_features	memory utilization	-0.20	[-0.31, -0.08]	1	Logs bounds checks dashboard
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.32	[-0.81, +0.16]	1	Logs
➖	idle	memory utilization	-0.38	[-0.42, -0.33]	1	Logs bounds checks dashboard
➖	otel_to_otel_logs	ingress throughput	-1.20	[-2.00, -0.40]	1	Logs

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
✅	quality_gate_idle	memory_usage	10/10
✅	quality_gate_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".

[scottopell]

/merge

[dd-devflow]

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