[network path] Avoid reporting unknown_hop_X for successful hops

[AlexandreYang]

What does this PR do?

[network path] Stop setting unknown hostname

Motivation

Since we are not doing Reverse DNS anymore for public IPs, I noticed that we are showing hostname like unknown_hop_3 in UI:

Related to this change this change in 7.60.0 the backend is now showing

Describe how to test/QA your changes

Verify that hop.hostname is not unknown_hop_X, when the hop is reachable.

Possible Drawbacks / Trade-offs

Additional Notes

[AlexandreYang]

/merge

[dd-devflow]

🚂 MergeQueue: waiting for PR to be ready

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Note: if you pushed new commits since the last approval, you may need additional approval.
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[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=48365460 --os-family=ubuntu

Note: This applies to commit 838de01

[dd-devflow]

🚂 MergeQueue: pull request added to the queue

The median merge time in main is 23m.

Use /merge -c to cancel this operation!

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 2bff8134-d4e7-483f-b0e0-fe1ec748246f

Baseline: a5528fa
Comparison: 838de01
Diff

Optimization Goals: ❌ Significant changes detected

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
❌	basic_py_check	% cpu utilization	+10.04	[+5.97, +14.11]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
❌	basic_py_check	% cpu utilization	+10.04	[+5.97, +14.11]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+1.75	[+1.61, +1.88]	1	Logs bounds checks dashboard
➖	file_tree	memory utilization	+1.71	[+1.56, +1.87]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	+1.24	[-2.32, +4.80]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+1.10	[+1.02, +1.18]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.14	[-0.34, +0.63]	1	Logs
➖	idle	memory utilization	+0.13	[+0.07, +0.19]	1	Logs bounds checks dashboard
➖	file_to_blackhole_0ms_latency	egress throughput	+0.02	[-0.32, +0.35]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.01	[-0.21, +0.22]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.00	[-0.17, +0.18]	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	[-0.12, +0.11]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.15	[-0.40, +0.09]	1	Logs
➖	quality_gate_idle	memory utilization	-0.47	[-0.53, -0.40]	1	Logs bounds checks dashboard
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-1.85	[-2.56, -1.14]	1	Logs
➖	idle_all_features	memory utilization	-1.85	[-1.99, -1.71]	1	Logs bounds checks dashboard

Bounds Checks: ❌ Failed

perf	experiment	bounds_check_name	replicates_passed	links
❌	idle	memory_usage	6/10	bounds checks dashboard
❌	quality_gate_idle	memory_usage	8/10	bounds checks dashboard
❌	idle_all_features	memory_usage	9/10	bounds checks dashboard
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	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	memory_usage	10/10
✅	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".