[usm] Move telemetry goroutine from network-tracer to the usm monitor

[guyarb]

What does this PR do?

Moves USM's telemetry goroutine from the network tracer to the USM monitor.

Motivation

Encapsulate all USM logic under its monitor.

Describe how you validated your changes

Possible Drawbacks / Trade-offs

Additional Notes

[agent-platform-auto-pr]

Static quality checks ✅

Please find below the results from static quality gates

Info

Result	Quality gate	On disk size	On disk size limit	On wire size	On wire size limit
✅	static_quality_gate_agent_deb_amd64	841.59MiB	858.45MiB	203.57MiB	214.3MiB
✅	static_quality_gate_docker_agent_amd64	925.99MiB	942.69MiB	309.87MiB	321.56MiB

[agent-platform-auto-pr]

Uncompressed package size comparison

Comparison with ancestor 8cba5f293f3ee1af7260338df9da39216a042bae

Diff per package

package	diff	status	size	ancestor	threshold
datadog-agent-amd64-deb	0.00MB	✅	870.17MB	870.17MB	0.50MB
datadog-agent-x86_64-rpm	0.00MB	✅	879.91MB	879.91MB	0.50MB
datadog-agent-x86_64-suse	0.00MB	✅	879.91MB	879.91MB	0.50MB
datadog-agent-arm64-deb	0.00MB	✅	858.07MB	858.07MB	0.50MB
datadog-agent-aarch64-rpm	0.00MB	✅	867.79MB	867.79MB	0.50MB
datadog-dogstatsd-amd64-deb	0.00MB	✅	59.10MB	59.10MB	0.50MB
datadog-dogstatsd-x86_64-rpm	0.00MB	✅	59.18MB	59.18MB	0.50MB
datadog-dogstatsd-x86_64-suse	0.00MB	✅	59.18MB	59.18MB	0.50MB
datadog-dogstatsd-arm64-deb	0.00MB	✅	56.57MB	56.57MB	0.50MB
datadog-heroku-agent-amd64-deb	0.00MB	✅	442.38MB	442.38MB	0.50MB
datadog-iot-agent-amd64-deb	0.00MB	✅	86.38MB	86.38MB	0.50MB
datadog-iot-agent-x86_64-rpm	0.00MB	✅	86.45MB	86.45MB	0.50MB
datadog-iot-agent-x86_64-suse	0.00MB	✅	86.45MB	86.45MB	0.50MB
datadog-iot-agent-arm64-deb	0.00MB	✅	82.65MB	82.65MB	0.50MB
datadog-iot-agent-aarch64-rpm	0.00MB	✅	82.72MB	82.72MB	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=55392720 --os-family=ubuntu

Note: This applies to commit f1ac2f4

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: dad86d7b-dacf-445c-b3be-7fa21b044f6b

Baseline: 8cba5f2
Comparison: f1ac2f4
Diff

Optimization Goals: ✅ Improvement(s) detected

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
✅	quality_gate_logs	% cpu utilization	-7.21	[-10.17, -4.24]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+1.53	[+0.65, +2.42]	1	Logs
➖	file_tree	memory utilization	+0.68	[+0.61, +0.75]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.30	[-0.47, +1.08]	1	Logs
➖	quality_gate_idle	memory utilization	+0.27	[+0.23, +0.30]	1	Logs bounds checks dashboard
➖	quality_gate_idle_all_features	memory utilization	+0.07	[-0.00, +0.14]	1	Logs bounds checks dashboard
➖	file_to_blackhole_0ms_latency	egress throughput	+0.04	[-0.84, +0.93]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	+0.01	[-0.45, +0.47]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.00	[-0.64, +0.64]	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	[-0.29, +0.29]	1	Logs
➖	file_to_blackhole_0ms_latency_http1	egress throughput	-0.01	[-0.88, +0.87]	1	Logs
➖	file_to_blackhole_0ms_latency_http2	egress throughput	-0.02	[-0.90, +0.87]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.03	[-0.74, +0.68]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.08	[-0.85, +0.70]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-1.99	[-2.09, -1.89]	1	Logs
✅	quality_gate_logs	% cpu utilization	-7.21	[-10.17, -4.24]	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_idle, 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_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.
• 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_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.

[hmahmood]

The reporter is in the protocol classification code? So is the best place not in the network tracer module?

[guyarb]

The reporter is in the protocol classification code? So is the best place not in the network tracer module?

I'm not aware of any usermode metrics related to the classification
Did I miss anything?

[hmahmood]

The reporter is in the protocol classification code? So is the best place not in the network tracer module?

I'm not aware of any usermode metrics related to the classification Did I miss anything?

Do you want to move the whole telemetry package under usm then?