[e2e] pin sles-12 and sles-15 AMIs

[pducolin]

What does this PR do?

• pin x86_64 Suse AMIs to our custom AMIs

Motivation

#incident-31580

Found out we were still using the marketplace AMI, new ones are

dd-agent-sles-12-x86_64

and dd-agent-sles-15-x86_64

Describe how to test/QA your changes

Will trigger a deploy pipeline to force execute the step by step test. As a follow up, agent-delivery will change the step by step rule, to run on step by step test changes and on platforms.json file changes

Possible Drawbacks / Trade-offs

Additional Notes

[pducolin]

/trigger-ci --variable RUN_ALL_BUILDS=true --variable RUN_KITCHEN_TESTS=true --variable RUN_E2E_TESTS=on --variable DEPLOY_AGENT=true --variable RUN_UNIT_TESTS=on --variable RUN_KMT_TESTS=on

[dd-devflow]

🚂 Gitlab pipeline started

Started pipeline #46803411

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Run ID: 782381a0-df5e-42d9-a807-4a8ffeb5624d Metrics dashboard Target profiles

Baseline: 4c43e25
Comparison: 4f443e1

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
➖	tcp_syslog_to_blackhole	ingress throughput	+1.48	[+1.43, +1.54]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.64	[+0.15, +1.13]	1	Logs
➖	file_tree	memory utilization	+0.18	[+0.05, +0.31]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.12	[-0.13, +0.37]	1	Logs
➖	otel_to_otel_logs	ingress throughput	+0.09	[-0.71, +0.90]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.00	[-0.22, +0.23]	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_300ms_latency	egress throughput	-0.02	[-0.20, +0.16]	1	Logs
➖	idle	memory utilization	-0.04	[-0.09, +0.00]	1	Logs bounds checks dashboard
➖	file_to_blackhole_0ms_latency	egress throughput	-0.05	[-0.38, +0.28]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	-0.25	[-2.74, +2.23]	1	Logs
➖	basic_py_check	% cpu utilization	-0.33	[-3.05, +2.38]	1	Logs
➖	idle_all_features	memory utilization	-0.38	[-0.49, -0.28]	1	Logs bounds checks dashboard
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.59	[-1.31, +0.12]	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

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".