[RC] Support alternative ALPN protocol for websocket tests

[mellon85]

Add new websocket test that negotiates dd-rc-v1 ALPN protocol during TLS handshake. This allows the agent to bypass hostile proxies and meshes that enforce streaming time limitations by making the connection appear as opaque TCP rather than HTTP.

The agent now runs two websocket echo tests in parallel:

• Standard test using default ALPN negotiation
• New test explicitly requesting dd-rc-v1 protocol

Both tests use the same endpoint and test logic. The server distinguishes between them via ALPN protocol tags in metrics, enabling separate monitoring of each connection type.

Changes are backward compatible. Existing tests continue to work unchanged.

RC-3097

[chatgpt-codex-connector]

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[domodwyer]

We should DRY this with the other impl - it's only 1 line of difference in config (setting the ALPN).

[dd-octo-sts]

Files inventory check summary

File checks results against ancestor fe5f79fe:

Results for datadog-agent_7.80.0~devel.git.53.f35283e.pipeline.108593864-1_amd64.deb:

No change detected

[cit-pr-commenter-54b7da]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: d693a9b9-f66c-4054-8411-96c000611d19

Baseline: fe5f79f
Comparison: f35283e
Diff

Optimization Goals: ✅ No significant changes detected

Experiments ignored for regressions

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

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	docker_containers_cpu	% cpu utilization	+1.14	[-1.82, +4.11]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	quality_gate_metrics_logs	memory utilization	+1.31	[+1.08, +1.54]	1	Logs bounds checks dashboard
➖	docker_containers_cpu	% cpu utilization	+1.14	[-1.82, +4.11]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+1.03	[+0.87, +1.19]	1	Logs
➖	uds_dogstatsd_20mb_12k_contexts_20_senders	memory utilization	+0.39	[+0.32, +0.45]	1	Logs
➖	docker_containers_memory	memory utilization	+0.24	[+0.15, +0.32]	1	Logs
➖	ddot_logs	memory utilization	+0.20	[+0.14, +0.26]	1	Logs
➖	ddot_metrics_sum_cumulativetodelta_exporter	memory utilization	+0.18	[-0.04, +0.40]	1	Logs
➖	ddot_metrics_sum_delta	memory utilization	+0.12	[-0.05, +0.30]	1	Logs
➖	otlp_ingest_logs	memory utilization	+0.10	[-0.00, +0.21]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.10	[-0.43, +0.62]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.09	[-0.03, +0.22]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.10, +0.10]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.21, +0.21]	1	Logs
➖	uds_dogstatsd_to_api_v3	ingress throughput	-0.01	[-0.23, +0.20]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.01	[-0.41, +0.38]	1	Logs
➖	file_tree	memory utilization	-0.05	[-0.11, +0.00]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.08	[-0.52, +0.37]	1	Logs
➖	otlp_ingest_metrics	memory utilization	-0.16	[-0.32, -0.00]	1	Logs
➖	quality_gate_idle	memory utilization	-0.19	[-0.25, -0.14]	1	Logs bounds checks dashboard
➖	ddot_metrics	memory utilization	-0.37	[-0.54, -0.19]	1	Logs
➖	quality_gate_logs	% cpu utilization	-0.44	[-2.09, +1.20]	1	Logs bounds checks dashboard
➖	quality_gate_idle_all_features	memory utilization	-0.45	[-0.48, -0.41]	1	Logs bounds checks dashboard
➖	ddot_metrics_sum_cumulative	memory utilization	-0.69	[-0.85, -0.54]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	observed_value	links
✅	docker_containers_cpu	simple_check_run	10/10	567 ≥ 26
✅	docker_containers_memory	memory_usage	10/10	275.96MiB ≤ 370MiB
✅	docker_containers_memory	simple_check_run	10/10	705 ≥ 26
✅	file_to_blackhole_0ms_latency	memory_usage	10/10	0.19GiB ≤ 1.20GiB
✅	file_to_blackhole_0ms_latency	missed_bytes	10/10	0B = 0B
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10	0.24GiB ≤ 1.20GiB
✅	file_to_blackhole_1000ms_latency	missed_bytes	10/10	0B = 0B
✅	file_to_blackhole_100ms_latency	memory_usage	10/10	0.20GiB ≤ 1.20GiB
✅	file_to_blackhole_100ms_latency	missed_bytes	10/10	0B = 0B
✅	file_to_blackhole_500ms_latency	memory_usage	10/10	0.22GiB ≤ 1.20GiB
✅	file_to_blackhole_500ms_latency	missed_bytes	10/10	0B = 0B
✅	quality_gate_idle	intake_connections	10/10	4 = 4	bounds checks dashboard
✅	quality_gate_idle	memory_usage	10/10	176.58MiB ≤ 181MiB	bounds checks dashboard
✅	quality_gate_idle_all_features	intake_connections	10/10	4 = 4	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	497.70MiB ≤ 550MiB	bounds checks dashboard
✅	quality_gate_logs	intake_connections	10/10	4 ≤ 6	bounds checks dashboard
✅	quality_gate_logs	memory_usage	10/10	206.32MiB ≤ 220MiB	bounds checks dashboard
✅	quality_gate_logs	missed_bytes	10/10	0B = 0B	bounds checks dashboard
✅	quality_gate_metrics_logs	cpu_usage	10/10	357.02 ≤ 2000	bounds checks dashboard
✅	quality_gate_metrics_logs	intake_connections	10/10	4 ≤ 6	bounds checks dashboard
✅	quality_gate_metrics_logs	memory_usage	10/10	423.81MiB ≤ 475MiB	bounds checks dashboard
✅	quality_gate_metrics_logs	missed_bytes	10/10	0B = 0B	bounds checks dashboard

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_metrics_logs, bounds check missed_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check cpu_usage: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, 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, bounds check memory_usage: 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.
• quality_gate_logs, bounds check missed_bytes: 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.