[CWS] Do not infer cgroup from container ID

[lebauce]

What does this PR do?

Remove wrong helpers that infer cgroup path simply from the container ID and
the container runtime.

Motivation

Cgroup path depends from the cgroup version, the distribution and configuration
so it's very hard to guess the cgroup path from the container ID and runtime.

Describe how you validated your changes

Possible Drawbacks / Trade-offs

Additional Notes

[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 create-vm --pipeline-id=50263093 --os-family=ubuntu

Note: This applies to commit bce270c

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 3e617589-c579-4736-82d9-3716e3760578

Baseline: 01c8ef8
Comparison: bce270c
Diff

Optimization Goals: ❌ Significant changes detected

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
✅	pycheck_lots_of_tags	% cpu utilization	-6.55	[-9.90, -3.20]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	quality_gate_logs	% cpu utilization	+2.60	[-0.38, +5.58]	1	Logs
➖	file_tree	memory utilization	+1.39	[+1.25, +1.52]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+0.99	[+0.85, +1.14]	1	Logs bounds checks dashboard
➖	tcp_syslog_to_blackhole	ingress throughput	+0.66	[+0.60, +0.72]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.21	[-0.56, +0.99]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.15	[-0.58, +0.88]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	+0.14	[-0.32, +0.60]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.07	[-0.66, +0.80]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.04	[-0.73, +0.81]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.04	[-0.85, +0.92]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.03	[-0.60, +0.65]	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	[-0.12, +0.11]	1	Logs
➖	otel_to_otel_logs	ingress throughput	-0.24	[-0.93, +0.46]	1	Logs
➖	quality_gate_idle	memory utilization	-0.51	[-0.59, -0.44]	1	Logs bounds checks dashboard
➖	basic_py_check	% cpu utilization	-0.57	[-4.51, +3.37]	1	Logs
✅	pycheck_lots_of_tags	% cpu utilization	-6.55	[-9.90, -3.20]	1	Logs

Bounds Checks: ❌ Failed

perf	experiment	bounds_check_name	replicates_passed	links
❌	quality_gate_idle	memory_usage	9/10	bounds checks dashboard
✅	file_to_blackhole_0ms_latency	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency	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_all_features	memory_usage	10/10	bounds checks dashboard
✅	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

❌ Failed. Some Quality Gates were violated.

• quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check memory_usage: 9/10 replicas passed. Failed 1 which is > 0. Gate FAILED.
• 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.

[spikat]

I thinks this is part from another PR, are you waiting it to be merged to rebase this one?

[lebauce]

Yes, the other PR should be merged soon