[EBPF-376]: split testdata between ebpf and telemetry pkgs

[val06]

What does this PR do?

split testdata directory between ebpf and ebpf/telemetry packages

Motivation

better code structure

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

Reviewer's Checklist

• If known, an appropriate milestone has been selected; otherwise the Triage milestone is set.
• Use the major_change label if your change either has a major impact on the code base, is impacting multiple teams or is changing important well-established internals of the Agent. This label will be use during QA to make sure each team pay extra attention to the changed behavior. For any customer facing change use a releasenote.
• A release note has been added or the changelog/no-changelog label has been applied.
• Changed code has automated tests for its functionality.
• Adequate QA/testing plan information is provided. Except if the qa/skip-qa label, with required either qa/done or qa/no-code-change labels, are applied.
• At least one team/.. label has been applied, indicating the team(s) that should QA this change.
• If applicable, docs team has been notified or an issue has been opened on the documentation repo.
• If applicable, the need-change/operator and need-change/helm labels have been applied.
• If applicable, the k8s/<min-version> label, indicating the lowest Kubernetes version compatible with this feature.
• If applicable, the config template has been updated.

[val06]

/merge

[dd-devflow]

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[pr-commenter]

Bloop Bleep... Dogbot Here

Regression Detector Results

Run ID: 8b2b6e57-e2d0-4396-aa65-3475969998e8
Baseline: 21acc30
Comparison: 6dfbfab
Total CPUs: 7

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.

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	idle	memory utilization	+0.41	[+0.38, +0.44]
➖	file_tree	memory utilization	+0.18	[+0.07, +0.29]
➖	file_to_blackhole	% cpu utilization	-0.04	[-6.61, +6.54]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	idle	memory utilization	+0.41	[+0.38, +0.44]
➖	process_agent_standard_check_with_stats	memory utilization	+0.32	[+0.26, +0.37]
➖	tcp_syslog_to_blackhole	ingress throughput	+0.25	[+0.18, +0.32]
➖	file_tree	memory utilization	+0.18	[+0.07, +0.29]
➖	trace_agent_json	ingress throughput	+0.04	[+0.01, +0.07]
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.06, +0.06]
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.03, +0.03]
➖	process_agent_real_time_mode	memory utilization	-0.01	[-0.05, +0.03]
➖	trace_agent_msgpack	ingress throughput	-0.03	[-0.05, -0.02]
➖	file_to_blackhole	% cpu utilization	-0.04	[-6.61, +6.54]
➖	process_agent_standard_check	memory utilization	-0.79	[-0.84, -0.73]
➖	otel_to_otel_logs	ingress throughput	-1.84	[-2.60, -1.08]

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".