[WIP] Add WLAN Integration #32530
[WIP] Add WLAN Integration #32530
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Regression DetectorRegression Detector ResultsMetrics dashboard Baseline: f3f94f9 Optimization Goals: ✅ No significant changes detected
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| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | quality_gate_idle | memory utilization | +0.38 | [+0.34, +0.42] | 1 | Logs bounds checks dashboard |
| ➖ | file_to_blackhole_1000ms_latency | egress throughput | +0.31 | [-0.47, +1.10] | 1 | Logs |
| ➖ | tcp_syslog_to_blackhole | ingress throughput | +0.24 | [+0.19, +0.30] | 1 | Logs |
| ➖ | file_tree | memory utilization | +0.22 | [+0.09, +0.35] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency_http1 | egress throughput | +0.07 | [-0.77, +0.90] | 1 | Logs |
| ➖ | file_to_blackhole_100ms_latency | egress throughput | +0.05 | [-0.65, +0.75] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency_http2 | egress throughput | +0.04 | [-0.77, +0.84] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api | ingress throughput | +0.01 | [-0.10, +0.12] | 1 | Logs |
| ➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.00 | [-0.01, +0.01] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency | egress throughput | -0.02 | [-0.91, +0.88] | 1 | Logs |
| ➖ | quality_gate_idle_all_features | memory utilization | -0.03 | [-0.12, +0.05] | 1 | Logs bounds checks dashboard |
| ➖ | file_to_blackhole_500ms_latency | egress throughput | -0.07 | [-0.84, +0.71] | 1 | Logs |
| ➖ | quality_gate_logs | % cpu utilization | -0.09 | [-3.34, +3.15] | 1 | Logs |
| ➖ | file_to_blackhole_300ms_latency | egress throughput | -0.10 | [-0.74, +0.55] | 1 | Logs |
| ➖ | file_to_blackhole_1000ms_latency_linear_load | egress throughput | -0.31 | [-0.78, +0.15] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -2.31 | [-2.99, -1.64] | 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 | memory_usage | 10/10 | bounds checks dashboard |
| ✅ | 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:
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Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
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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.
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Its configuration does not mark it "erratic".
CI Pass/Fail Decision
✅ Passed. All Quality Gates passed.
- quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate 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_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
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/trigger-ci --variable RUN_ALL_BUILDS=true --variable RUN_KITCHEN_TESTS=true --variable RUN_E2E_TESTS=on --variable RUN_UNIT_TESTS=on --variable RUN_KMT_TESTS=on |
Devflow running:
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/trigger-ci --variable RUN_ALL_BUILDS=true --variable RUN_KITCHEN_TESTS=true --variable RUN_E2E_TESTS=on --variable RUN_UNIT_TESTS=on --variable RUN_KMT_TESTS=on |
Devflow running:
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Go Package Import DifferencesBaseline: f3f94f9
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/trigger-ci --variable RUN_ALL_BUILDS=true --variable RUN_KITCHEN_TESTS=off --variable RUN_E2E_TESTS=off --variable RUN_UNIT_TESTS=off --variable RUN_KMT_TESTS=off |
Devflow running:
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/trigger-ci --variable RUN_ALL_BUILDS=true --variable RUN_KITCHEN_TESTS=off --variable RUN_E2E_TESTS=off --variable RUN_UNIT_TESTS=off --variable RUN_KMT_TESTS=off --variable MACOSX_DEPLOYMENT_TARGET=10.15 |
Devflow running:
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/trigger-ci --variable RUN_ALL_BUILDS=true --variable RUN_KITCHEN_TESTS=off --variable RUN_E2E_TESTS=off --variable RUN_UNIT_TESTS=off --variable RUN_KMT_TESTS=off |
Devflow running:
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Test changes on VMUse this command from test-infra-definitions to manually test this PR changes on a VM: inv aws.create-vm --pipeline-id=51936024 --os-family=ubuntuNote: This applies to commit 008a33d9 |
Uncompressed package size comparisonComparison with ancestor Diff per package
Decision |
What does this PR do?
Adds a WLAN integration as a corecheck in the agent.
Motivation
Monitor the current Wi-Fi interface in use and collect various metrics to check the health of the Wi-Fi connection.
For now, the check collects the following data:
Describe how you validated your changes
Possible Drawbacks / Trade-offs
Additional Notes