[RELENG-7422] 📝 Add documentation

docs/metrics-analysis-prometheus/collected-reported-metrics.md

@@ -88,24 +88,33 @@ We aim to obtain a result that is close to reality, within a range of
 approximately +/- 5%, for data visualization purposes.
 Key points to consider for retrieving cost information:
 
-- RAM and CPU Costs : provided values for RAM and CPU expenses, can be found
-  in the Google Cloud documentation.
-- Storage Costs : provided values for storage expenses, can be found in the
-  Google Cloud documentation.
-- Bandwidth Cost: Directly determining the cost of bandwidth is not feasible.
-
-To arrive at an approximate cost, we conducted an analysis of previous invoices
-and calculated the additional expenses incurred due to bandwidth, which averaged
-around 30% per month. With this information, we were able to approximate the
-overall cost using the following formula:
+- RAM and CPU Costs : provided cost per minute for RAM and CPU expenses, can
+  be found in the documentation of the respective cloud provider.
+- Storage Costs : provided cost per minute for storage expenses, can
+  be found in the documentation of the respective cloud provider.
+- Bandwidth Cost: Directly determining the cost per minute of bandwidth is
+  not feasible.
+
+Calculating the bandwidth cost per minutes is up to the discretion of the
+user and will vary depending on the workload. As an example, adding an
+extra 30% is what we found by comparing the values in the documentation
+of different cloud providers (for CPU, RAM, and storage) with the actual
+values available on our invoices. Using this information, we were able
+to estimate the overall cost using the following formula:
+(all costs are per minute)
 
 ```bash
-cost = (cost_per_flavor + cost_per_storage) * 130 / 100
+cost = (cost_per_flavor + cost_per_storage) * cost_of_bandwidth / 100
 ```
 
+<!-- trunk:ignore
+
 !!! note
+
     GCP and AWS costs are quite the same for the same flavors.
 
+-->
+
 ### The different tags and their associated cost
 
 | Provider | Runner               | Cost ($ per min) |
@@ -123,7 +132,12 @@ cost = (cost_per_flavor + cost_per_storage) * 130 / 100
 | GCP      | `n2-standard-4`      | 0.005            |
 | GCP      | `n2-standard-8`      | 0.01             |
 
+<!-- trunk:ignore
+
 !!! note
+
     Please note that the names of large GitHub hosted runners
     may not be explicitly the same as shown below, but this is
     the naming convention recommended by GitHub.
+
+-->

docs/metrics-analysis-prometheus/prometheus.md

@@ -2,13 +2,13 @@
 
 ## Introduction
 
-In order to collect and analyze GitHub Actions metrics, users are expected
-to have an existing Prometheus installation and configure it to pull metrics.
-
 Prometheus is a powerful open-source monitoring and alerting system that allows
 users to collect, store, and analyze time-series data. In this guide, we will
 explore how to effectively utilize Prometheus to analyze GitHub Actions.
 
+In order to collect and analyze GitHub Actions metrics, users are expected
+to have an existing Prometheus installation and configure it to pull metrics.
+
 ## Understanding Prometheus Queries
 
 The idea here is not to recreate the entire Prometheus documentation; we will
@@ -39,16 +39,21 @@ within a specified time range.
    cumulative sum of the github_actions_job_cost_count_total metric,
    representing the total job cost count.
 3. The `[5m]` part specifies the time range for the query.
-4. The `by (repository)` clause groups the data by the repository field.
+4. The `by (repository)` clause groups the data by the repository label.
    This enables the query to calculate the cost sum for each repository individually.
 5. The expression `> 0` filters the query results to only include
    repositories with a value greater than zero.
 
+<!-- trunk:ignore
+
 !!! info
-   You can also use Grafana, it enhances the visualization of Prometheus data and
-   provides powerful querying capabilities. Within Grafana, you can apply filters,
-   combine queries, and utilize variables for dynamic filtering. It's important
-   to understand `__interval` (time interval between data points) and `__range`
-   (selected time range) when working with Prometheus data in Grafana. This
-   integration enables efficient data exploration and analysis for better
-   insights and decision-making.
+
+      Using Grafana enhances the visualization of Prometheus data and
+      provides powerful querying capabilities. Within Grafana, apply filters,
+      combine queries, and utilize variables for dynamic filtering. It's important
+      to understand `__interval` (time interval between data points) and `__range`
+      (selected time range) when working with Prometheus data in Grafana. This
+      integration enables efficient data exploration and analysis for better
+      insights and decision-making.
+
+-->