Akarpowich/test other syscalls

[akarpz]

What does this PR do?

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=44177201 --os-family=ubuntu

Note: This applies to commit 0f919554

[pr-commenter[bot]]

Regression Detector

Regression Detector Results

Run ID: c9a8d076-49c3-43f0-a201-b153d57d67a9 Metrics dashboard Target profiles

Baseline: 18ad7b352bfff8a8e2f8279e3289a4b0fb2c8c0f Comparison: 0f9195548626a946fff1e95a243363104c0e0e7b

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	+1.30	[+1.24, +1.35]	1	Logs
➖	otel_to_otel_logs	ingress throughput	+0.18	[-0.65, +1.01]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.00, +0.00]	1	Logs
➖	file_tree	memory utilization	-0.08	[-0.21, +0.04]	1	Logs
➖	idle	memory utilization	-0.09	[-0.15, -0.03]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.38	[-1.14, +0.37]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	-0.44	[-3.04, +2.15]	1	Logs
➖	basic_py_check	% cpu utilization	-0.55	[-3.38, +2.27]	1	Logs

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
❌	idle	memory_usage	7/10

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".