Load Balancing for AI Model APIs on GCP

Question

Pulumi · Accepted Answer

To set up load balancing for AI Model APIs on GCP using Pulumi, we need to create and configure several resources that will collectively form the load balancer. In Google Cloud, this typically involves the following steps:

1. **Create a Backend Service**: A backend service defines how Google Cloud load balancers distribute incoming traffic to the instances.

2. **Create a Health Check**: Health checks ensure traffic is only sent to healthy instances capable of serving requests.

3. **Set up instance groups**: Instance groups are collections of virtual machine instances that can be accessed through the load balancer.

4. **Create a URL Map**: URL maps direct incoming requests to the appropriate backend based on factors such as URL paths or hostnames.

5. **Create a Target Proxy**: This will receive incoming requests and forward them to the URL map.

6. **Create a Forwarding Rule**: This rule governs the load balancer's frontend configuration, like IP address, port numbers, and global vs regional behavior.

7. **Create an Autoscaler (Optional)**: Autoscalers automatically adjust the number of instances in your instance group according to the load.

Here is a program that sets up a basic HTTP load balancer for AI Model APIs on GCP. The structure of the program includes imports of required packages, creation of resources, and exporting final values of the resources. For an HTTPS load balancer, additional resources like SSL certificates would be necessary.

```python
import pulumi
import pulumi_gcp as gcp

# Create a Backend Service, with a Health Check to ensure traffic only goes to healthy instances
backend_service = gcp.compute.BackendService("backendService",
    health_checks=[health_check.self_link],
    backends=[{
        "group": instance_group.self_link,
    }],
)

# Define a Health Check to attach to the Backend Service
health_check = gcp.compute.HealthCheck("healthCheck",
    http_health_check={
        "port": 80,
        "request_path": "/api/health"  # The endpoint used by the load balancer to perform health checks.
    }
)

# Define an Instance Group, which would contain your backend instances serving the AI Model API
instance_group = gcp.compute.InstanceGroup("instanceGroup",
    instances=[instance1.id, instance2.id],
    named_port=[{
        "name": "http",  # This named port maps to the port used by the Backend Service for health checks
        "port": 80
    }],
    # You would specify the zone where you want your instances to reside, e.g., "us-central1-a"
    zone="your-zone-here",
)

# Define a URL Map to route the incoming requests to appropriate backend service
url_map = gcp.compute.URLMap("urlMap",
    default_service=backend_service.self_link,
    # You can further define path matchers if needed for more complex routing rules.
)

# Define a Target HTTP Proxy to use the URL Map and field incoming requests
http_proxy = gcp.compute.TargetHttpProxy("httpProxy",
    url_map=url_map.self_link
)

# Define a Global Forwarding Rule to tie everything together and serve traffic
forwarding_rule = gcp.compute.GlobalForwardingRule("forwardingRule",
    ip_protocol="TCP",
    port_range="80",
    target=http_proxy.self_link,
    # By leaving the IP address blank, GCP will provision an ephemeral external IP for you
)

# (Optional) Define an Autoscaler to automatically adjust the size of the instance group
autoscaler = gcp.compute.Autoscaler("autoscaler",
    target=instance_group.self_link,
    autoscaling_policy={
        "max_replicas": 5,
        "min_replicas": 1,
        "cooldown_period": 60,  # The number of seconds to wait before collecting new metrics after changes to the instance group
        "cpu_utilization": {
            "target": 0.6  # Target CPU utilization before scaling out. (60% CPU utilization)
        },
    },
    # You would specify the zone for the autoscaler corresponding to the instance group zone.
    zone="your-zone-here",
)

# Export the IP address of the Forwarding Rule to know where to point your requests
pulumi.export("load_balancer_ip", forwarding_rule.ip_address)
```

In the above program, you create the necessary components to set up a basic HTTP load balancer on GCP. Please note that to run this code successfully, you will need to replace placeholder values like `your-zone-here` with the actual data for your setup and ensure that your instances (referenced by `instance1.id, instance2.id`) are set up to serve your AI Model API.

Each resource block represents a GCP resource that will be provisioned by Pulumi when you run the deployment process. For instance, `gcp.compute.BackendService` is a representation of a GCP Backend Service, which can be further configured with health checks and connected to instance groups for handling the backend traffic.

Remember to include the actual health check endpoint `"/api/health"` if the AI Model APIs have a health check implemented, or adjust the endpoint accordingly.

The `pulumi.export` statement at the end is used to output the IP address of the forwarding rule after the deployment, which can be used to access the load balanced service.