Add ddprocmon service status to flare

[clarkb7]

What does this PR do?

Add ddprocmon service status to flare

Make user-mode service match case insensitive, so it finds fleet automation service Datadog Installer.

Motivation

https://datadoghq.atlassian.net/browse/WINA-1267

Describe how you validated your changes

Generate flare, ensure that servicestatus.json includes ddprocmon

with fleet automation isntalled, ensure it included Datadog Installer

unit test added

Possible Drawbacks / Trade-offs

Additional Notes

windows treats service names case insensitively, too

The service control manager database preserves the case of the characters, but service name comparisons are always case insensitive.

https://learn.microsoft.com/en-us/windows/win32/api/winsvc/nf-winsvc-openservicea

[agent-platform-auto-pr]

Uncompressed package size comparison

Comparison with ancestor 6ce5f0040cbb506031834320ae64eff47b424a57

Diff per package

package	diff	status	size	ancestor	threshold
datadog-agent-arm64-deb	0.00MB	✅	914.54MB	914.54MB	0.50MB
datadog-agent-aarch64-rpm	0.00MB	✅	924.19MB	924.18MB	0.50MB
datadog-dogstatsd-amd64-deb	0.00MB	✅	58.95MB	58.95MB	0.50MB
datadog-dogstatsd-x86_64-rpm	0.00MB	✅	59.02MB	59.02MB	0.50MB
datadog-dogstatsd-x86_64-suse	0.00MB	✅	59.02MB	59.02MB	0.50MB
datadog-dogstatsd-arm64-deb	0.00MB	✅	56.46MB	56.46MB	0.50MB
datadog-heroku-agent-amd64-deb	0.00MB	✅	478.37MB	478.37MB	0.50MB
datadog-iot-agent-amd64-deb	0.00MB	✅	94.01MB	94.01MB	0.50MB
datadog-iot-agent-x86_64-rpm	0.00MB	✅	94.08MB	94.08MB	0.50MB
datadog-iot-agent-x86_64-suse	0.00MB	✅	94.08MB	94.08MB	0.50MB
datadog-iot-agent-arm64-deb	0.00MB	✅	90.05MB	90.05MB	0.50MB
datadog-iot-agent-aarch64-rpm	0.00MB	✅	90.12MB	90.12MB	0.50MB
datadog-agent-x86_64-rpm	-0.01MB	✅	937.11MB	937.12MB	0.50MB
datadog-agent-x86_64-suse	-0.01MB	✅	937.11MB	937.12MB	0.50MB
datadog-agent-amd64-deb	-0.01MB	✅	927.45MB	927.46MB	0.50MB

Decision

✅ Passed

[agent-platform-auto-pr]

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv aws.create-vm --pipeline-id=53400090 --os-family=ubuntu

Note: This applies to commit 86800f3

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 6010f62e-c30a-4a3f-9db2-eb548a386fc1

Baseline: 6ce5f00
Comparison: 86800f3
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	tcp_syslog_to_blackhole	ingress throughput	+0.58	[+0.51, +0.66]	1	Logs
➖	file_tree	memory utilization	+0.55	[+0.41, +0.70]	1	Logs
➖	quality_gate_idle	memory utilization	+0.27	[+0.24, +0.31]	1	Logs bounds checks dashboard
➖	uds_dogstatsd_to_api	ingress throughput	+0.02	[-0.08, +0.13]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.02	[-0.68, +0.72]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.01	[-0.89, +0.92]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.00	[-0.64, +0.63]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.00	[-0.80, +0.79]	1	Logs
➖	file_to_blackhole_0ms_latency_http2	egress throughput	-0.02	[-0.88, +0.84]	1	Logs
➖	file_to_blackhole_0ms_latency_http1	egress throughput	-0.02	[-0.95, +0.90]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	-0.07	[-0.54, +0.39]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.11	[-0.90, +0.67]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-0.17	[-0.25, -0.08]	1	Logs bounds checks dashboard
➖	quality_gate_logs	% cpu utilization	-0.95	[-4.14, +2.25]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-1.00	[-1.72, -0.28]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	file_to_blackhole_0ms_latency	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_0ms_latency_http1	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency_http1	memory_usage	10/10
✅	file_to_blackhole_0ms_latency_http2	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency_http2	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency_linear_load	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	lost_bytes	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_300ms_latency	lost_bytes	10/10
✅	file_to_blackhole_300ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	lost_bytes	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	quality_gate_idle	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_idle	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_logs	intake_connections	10/10
✅	quality_gate_logs	lost_bytes	10/10
✅	quality_gate_logs	memory_usage	10/10

Explanation

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

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".

CI Pass/Fail Decision

✅ Passed. All Quality Gates passed.

• quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.

[iglendd]

should not be srvc.Close() move inside non-err scrope?