Use FinishedAt for container.uptime calculation if the container has stopped

[keisku]

What does this PR do?

Use finished at for uptime calculation if the container has stopped

Motivation

Currently, Agent calculates uptime by now - started at.
now - started at sometimes exceed finished at - started at.

container.uptime should be 60 seconds.

cat <<EOF | kubectl apply -f -
apiVersion: v1
kind: Pod
metadata:
  name: restart-count-test
spec:
  containers:
    - name: startstop
      image: busybox
      command: ["sh", "-c", "sleep 60 && exit 1"]
EOF

container.uptime was 64 seconds.

finishedAt: "2024-11-16T01:46:12Z" - startedAt: "2024-11-16T01:45:12Z" = 60 seconds

kubectl get pod restart-count-test -o yaml
....
  containerStatuses:
  - containerID: containerd://384db435a66e1fd719f717230a54c05d360418402c618878c21df55cba9383af
    image: docker.io/library/busybox:latest
    imageID: docker.io/library/busybox@sha256:768e5c6f5cb6db0794eec98dc7a967f40631746c32232b78a3105fb946f3ab83
    lastState:
      terminated:
        containerID: containerd://384db435a66e1fd719f717230a54c05d360418402c618878c21df55cba9383af
        exitCode: 1
        finishedAt: "2024-11-16T01:46:12Z"
        reason: Error
        startedAt: "2024-11-16T01:45:12Z"

Describe how to test/QA 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=49216344 --os-family=ubuntu

Note: This applies to commit 0d4862c

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 2b0c079f-c927-4b4a-9cdf-38fcbb3b0bf0

Baseline: bd393fa
Comparison: 0d4862c
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	pycheck_lots_of_tags	% cpu utilization	+2.76	[-0.84, +6.36]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.96	[+0.24, +1.68]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	+0.34	[-0.12, +0.79]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.32	[-0.46, +1.10]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.26	[-0.51, +1.03]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+0.21	[+0.10, +0.33]	1	Logs bounds checks dashboard
➖	tcp_syslog_to_blackhole	ingress throughput	+0.20	[+0.13, +0.28]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.13	[-0.50, +0.76]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.13	[-0.59, +0.84]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.07	[-0.73, +0.86]	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	[-0.11, +0.10]	1	Logs
➖	otel_to_otel_logs	ingress throughput	-0.52	[-1.16, +0.12]	1	Logs
➖	quality_gate_idle	memory utilization	-0.55	[-0.60, -0.51]	1	Logs bounds checks dashboard
➖	basic_py_check	% cpu utilization	-0.74	[-4.49, +3.01]	1	Logs
➖	file_tree	memory utilization	-0.74	[-0.88, -0.61]	1	Logs

Bounds Checks: ❌ Failed

perf	experiment	bounds_check_name	replicates_passed	links
❌	file_to_blackhole_1000ms_latency	lost_bytes	0/10
❌	file_to_blackhole_0ms_latency	lost_bytes	8/10
❌	file_to_blackhole_300ms_latency	lost_bytes	9/10
✅	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	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	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	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_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.