usm: Increase process monitor channel length

[vitkyrka]

What does this PR do?

Motivation

Describe how to test/QA your changes

Possible Drawbacks / Trade-offs

Additional Notes

[pr-commenter]

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=46046015 --os-family=ubuntu

Note: This applies to commit 4b1be8d

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 0393bf81-c01b-4a42-a9f8-b5fd15b3b346

Baseline: c5a00be
Comparison: 7bbe337

Regression Detector: ✅

Bounds Checks: ✅

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
➖	basic_py_check	% cpu utilization	+4.81	[+0.71, +8.91]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.21	[+0.13, +0.28]	1	Logs
➖	idle_all_features	memory utilization	+0.14	[+0.05, +0.23]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.01	[-0.11, +0.09]	1	Logs
➖	file_tree	memory utilization	-0.39	[-0.50, -0.27]	1	Logs
➖	otel_to_otel_logs	ingress throughput	-0.42	[-1.23, +0.38]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	-0.59	[-4.09, +2.91]	1	Logs
➖	idle	memory utilization	-0.72	[-0.76, -0.67]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.75	[-1.47, -0.03]	1	Logs

Bounds Checks Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	idle	memory_usage	10/10

Explanation

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

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".