Automated Uptime Alerting for AI Inference Services

Question

Pulumi · Accepted Answer

To set up automated uptime alerting for AI inference services, you would typically use a monitoring service provided by a cloud provider to watch the endpoint that the AI inference service is accessible at. If the service goes down or is unreachable, the monitoring service would trigger an alert. You can set up notifications to be sent out via email, SMS, or even integrate with communication platforms like Slack.

Pulumi allows us to define and manage this alerting and monitoring as part of our infrastructure as code. Below, I will detail how to set up a Google Cloud uptime check with alerts and notifications using Pulumi and Python.

### Google Cloud Uptime Checks and Alert Policy

We'll use two key resources from the Google Cloud Pulumi provider:
1. **`UptimeCheckConfig`**: This resource is a check that periodically sends requests to (or attempts a connection with) the chosen resource to verify it is online.
2. **`AlertPolicy`**: This resource allows defining policies that will be triggered when conditions of the uptime check fail, leading to notifications being sent out.

When combined, these resources can automate monitoring and alerting for AI inference endpoints or other web services.

Let's start with the Pulumi program:

```python
import pulumi
import pulumi_gcp as gcp

# Configure the Google Cloud Uptime Check
uptime_check = gcp.monitoring.UptimeCheckConfig("ai-inference-uptime-check",
    # DisplayName is a human-readable name for the uptime check
    display_name="AI Inference Service Uptime Check",
    # The HTTP Check configuration performs an HTTP GET request to confirm service availability
    http_check=gcp.monitoring.UptimeCheckConfigHttpCheckArgs(
        path="/v1/models/{model_name}:predict",  # Change this path to the specific endpoint of your service
        port=443,  # Most services will use SSL, and hence port 443
        use_ssl=True,
    ),
    # Set the period of the check. This configuration performs a check every 5 minutes
    period="300s",  # "s" denotes seconds
    # Set the timeout for the check. If a response is not received within this time, it's considered a failure
    timeout="10s",  # "s" denotes seconds
    # The resource that the uptime check should be concerned with; typically, a URL or specific service endpoint
    resource=gcp.monitoring.UptimeCheckConfigResourceArgs(
        type="uptime_url",
        labels={"host": "www.example.com"},  # Replace with your AI inference service host
    ),
    # Select regions from where the check should originate
    selected_regions=["USA"],  # You can choose multiple regions as needed
)

# Configure an Alert Policy for when the uptime check fails
alert_policy = gcp.monitoring.AlertPolicy("ai-inference-alert-policy",
    # DisplayName is a human-readable name for the alert policy
    display_name="AI Inference Service Availability Alert",
    # The conditions that will trigger the alert
    conditions=[gcp.monitoring.AlertPolicyConditionArgs(
        display_name="AI Inference Service Down",
        condition_threshold=gcp.monitoring.AlertPolicyConditionConditionThresholdArgs(
            # The type of comparison to perform; in this case, we want to know if the service is down
            comparison="COMPARISON_GT",
            # The duration for which the condition must be met
            duration="300s",
            # Set the value threshold for the alert condition. This signifies that any latency above 0 is considered
            value_threshold=0,
            # The data aggregations for this alert, e.g., latency, uptime, etc.
            aggregations=[gcp.monitoring.AlertPolicyConditionConditionThresholdAggregationArgs(
                alignment_period="60s",
                per_series_aligner="ALIGN_MEAN"
            )],
            # Ties the AlertPolicy to the UptimeCheckConfig via the uptime check's name
            monitoring_query_language="FETCH uptime_url
| FILTER (resource.label['host'] == 'www.example.com')
| REDUCE_FRACTION_OVER_TIME('1.0m', 100)
",
        ),
    )],
    # Notification channels (e.g., email, Slack, SMS, PagerDuty) can be added here
    # Note: Create Notification Channels using the gcp.monitoring.NotificationChannel resource and reference them here
    notification_channels=[],  # Add your notification channels
    # The combiner operation if multiple conditions are provided; since we only have one, "OR" is fine
    combiner="OR"
)

# Output the created AlertPolicy's name for reference
pulumi.export("alert_policy_name", alert_policy.display_name)
```

In the above program, you need to replace `{model_name}` in the `path` with your model's name, and `www.example.com` with your AI inference service's endpoint host. Additionally, you should provide your own notification channels to the `notification_channels` field of the `AlertPolicy` resource.

### Explanation:
- We define an HTTP check that attempts to connect to our AI service's endpoint.
- The check is configured to run every five minutes and wait for a maximum of 10 seconds for the service to respond.
- The alert policy is linked to this uptime check, and is triggered if the service goes down, sending notifications to the configured channels.
- We export the alert policy's name, so we can easily reference it in our Pulumi stack.

With these configurations, if our AI inference service becomes unreachable, the uptime check will fail, triggering the alert policy and notifying us through the specified channels.