[e2e] update centos-79 arm64 AMI

[pducolin]

What does this PR do?

Update AMIs for CentOS 79 arm64

Motivation

On July 1st, 2024 CentOS entered EOL and dropped their legacy mirrorlist. This broke few e2e and kitchen tests. The new AMI is a clone of the old one with mirrorlist updated

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

I created a VM from test-infra-definitions with

inv create-vm --os-family=centos --no-install-agent --architecture=arm64    

ssh into it, and ran

sudo yum update

ensuring it was working fine

[agent-platform-auto-pr[bot]]

[Fast Unit Tests Report]

On pipeline 38126552 (CI Visibility). The following jobs did not run any unit tests:

Jobs:

- tests_deb-arm64-py3 - tests_deb-x64-py3 - tests_flavor_dogstatsd_deb-x64 - tests_flavor_heroku_deb-x64 - tests_flavor_iot_deb-x64 - tests_rpm-arm64-py3 - tests_rpm-x64-py3 - tests_windows-x64

If you modified Go files and expected unit tests to run in these jobs, please double check the job logs. If you think tests should have been executed reach out to #agent-devx-help

[pr-commenter[bot]]

Regression Detector

Regression Detector Results

Run ID: 7976ec58-aa54-4ca1-97c6-9b360dd09db9 Metrics dashboard Target profiles

Baseline: 671f875b21bf84ec8b0d44cdd8cf1d450d808dc4 Comparison: e942070034c244e6e6295f4802a39b603aa81f08

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

No significant changes in experiment optimization goals

Confidence level: 90.00% Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Fine details of change detection per experiment

| perf | experiment | goal | Δ mean % | Δ mean % CI | links | |------|----------------------------|--------------------|----------|------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +1.24 | [+0.35, +2.12] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A7976ec58-aa54-4ca1-97c6-9b360dd09db9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719921134000&to_ts=1719932534000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | +0.94 | [-11.88, +13.75] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A7976ec58-aa54-4ca1-97c6-9b360dd09db9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719921134000&to_ts=1719932534000&live=false) | | ➖ | idle | memory utilization | +0.53 | [+0.49, +0.56] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3A7976ec58-aa54-4ca1-97c6-9b360dd09db9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719921134000&to_ts=1719932534000&live=false) | | ➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.00 | [-0.01, +0.01] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_dd_logs_filter_exclude%20run_id%3A7976ec58-aa54-4ca1-97c6-9b360dd09db9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719921134000&to_ts=1719932534000&live=false) | | ➖ | uds_dogstatsd_to_api | ingress throughput | -0.00 | [-0.00, +0.00] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api%20run_id%3A7976ec58-aa54-4ca1-97c6-9b360dd09db9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719921134000&to_ts=1719932534000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | -0.05 | [-4.85, +4.76] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3A7976ec58-aa54-4ca1-97c6-9b360dd09db9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719921134000&to_ts=1719932534000&live=false) | | ➖ | file_tree | memory utilization | -0.24 | [-0.28, -0.19] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A7976ec58-aa54-4ca1-97c6-9b360dd09db9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719921134000&to_ts=1719932534000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | -0.36 | [-1.17, +0.44] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A7976ec58-aa54-4ca1-97c6-9b360dd09db9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719921134000&to_ts=1719932534000&live=false) | | ➖ | basic_py_check | % cpu utilization | -0.47 | [-3.15, +2.21] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A7976ec58-aa54-4ca1-97c6-9b360dd09db9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719921134000&to_ts=1719932534000&live=false) |

Explanation

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".

[pducolin]

/merge

[dd-devflow[bot]]

:steam_locomotive: MergeQueue: pull request added to the queue

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