1. AI System Health Checks Using GCP Monitoring AlertPolicy


    In Google Cloud Platform (GCP), you can use Monitoring Alert Policies to watch the behavior of your cloud resources and notify you when something goes wrong. Alert policies in GCP Monitoring allow you to define criteria for what constitutes an unhealthy state, alongside specifying how and who should be notified if those criteria are met.

    To set this up, we'll use the Pulumi gcp.monitoring.AlertPolicy resource. This resource allows us to configure an alert policy that defines conditions under which an alert will be triggered and notifications will be sent through channels such as email, SMS, or other supported notification endpoints.

    Here's a step-by-step guide to creating an AI system health check using GCP Monitoring AlertPolicy in Pulumi:

    1. Import the GCP package: Include the GCP package in the Pulumi program to utilize the resources provided by Google Cloud.
    2. Create an AlertPolicy: Define an AlertPolicy with conditions that specify what metric or event will trigger the alert.
    3. Set up Notification Channels: Alerts need to notify you or your team. This is done via notification channels that you need to set up in GCP and reference in your Alert Policy.
    4. Export the AlertPolicy ID: This will allow you to easily reference or modify the alert policy in the future using Pulumi.

    Below is a python program that illustrates these steps, creating a simple alert policy that will trigger if a specified metric (like CPU usage) falls outside of the expected range:

    import pulumi import pulumi_gcp as gcp # Assuming you already have the Notification Channel created and have its ID, # replace 'your-notification-channel-id' with your actual channel ID. notification_channel_id = 'your-notification-channel-id' # Define an Alert Policy alert_policy = gcp.monitoring.AlertPolicy("example-alert-policy", combiner="OR", # Define how conditions should be combined (OR/AND) conditions=[{ "displayName": "High CPU Usage", # Human-readable name for the condition "conditionThreshold": { "filter": "metric.type=\"compute.googleapis.com/instance/cpu/utilization\"", "duration": "180s", # The period over which the metric is evaluated "comparison": "COMPARISON_GT", # Check if the value is greater than threshold "thresholdValue": 0.8, # 80% CPU usage threshold "aggregations": [{ "alignmentPeriod": "60s", # The period for aligning data points "perSeriesAligner": "ALIGN_RATE", # Method for aligning data points }], } }], # Link alert policy with a notification channel notificationChannels=[notification_channel_id], # Additional settings such as labels, documentation, etc., can be added here. ) # Export the ID of the new Alert Policy so you can easily find it later. pulumi.export('alert_policy_id', alert_policy.id)

    This program first defines the notification channel ID you have created in your GCP console under Monitoring > Notification Channels. The heart of this program is the AlertPolicy which watches for high CPU usage over a 3-minute window ("duration": "180s"), checking if usage is consistently above 80% during that time. You set the combiner to 'OR' to combine multiple conditions, if there are more than one.

    This basic threshold-based example is a starting point. GCP AlertPolicy also supports more advanced configurations, such as monitoring specific instances, custom metrics, log-based metrics, etc.

    Remember that to apply the code to your GCP project using Pulumi, you will need to have Pulumi CLI installed, be authenticated to your GCP account, and have a Pulumi stack initialized.