SSL Termination for AI APIs with GCP Target HTTPS Proxy

Question

Pulumi · Accepted Answer

SSL termination is a process where the SSL connection is terminated at the load balancer instead of being passed through to the backend servers. This means that the decryption of HTTPS traffic occurs at the load balancer, allowing the backend to handle unencrypted traffic and offloading the encryption overhead from the backend servers. This can lead to better performance, easier SSL certificate management, and the ability to inspect traffic at the load balancer level.

In Google Cloud, SSL termination typically happens at the load balancer level using a Target HTTPS Proxy. The Target HTTPS Proxy is a component that, together with URL maps and Backend Services, routes HTTPS requests from users to your backends.

To implement SSL termination for AI APIs using GCP Target HTTPS Proxy, we will perform the following steps:

1. Define an SSL certificate to prove the identity of the backend service and to facilitate encrypted connections.
2. Set up a Target HTTPS Proxy to receive HTTPS connections and provide SSL termination.
3. Create a Backend Service which will be the destination of the traffic after SSL termination.
4. Generate a URL Map to map incoming requests to the Backend Service.
5. Set up the Forwarding Rules to connect the Target HTTPS Proxy with the internet.

Now, let's put this into a Pulumi program written in Python to create an SSL-terminated setup using GCP resources:

```python
import pulumi
import pulumi_gcp as gcp

# Assume you already have an AI API Backend Service created or you can define one.
# In this scenario, let's assume the backend service is already defined and has an identifier backend_service_id.
# We will use a pre-existing managed SSL certificate for this example. Replace 'certificate_name' with your certificate's name.
ssl_certificate = gcp.compute.ManagedSslCertificate("ssl-certificate",
                                                     name="certificate_name")

# Define the Target HTTPS Proxy
target_https_proxy = gcp.compute.TargetHttpsProxy("target-https-proxy",
                                                  ssl_certificates=[ssl_certificate.self_link],
                                                  url_map=url_map.self_link)

# Create a URL Map to route the incoming requests to your backend service.
url_map = gcp.compute.UrlMap("url-map",
                             default_service=gcp.compute.BackendService.get(backend_service_id))

# Define a Global Forwarding Rule to route requests to the Target HTTPS Proxy
forwarding_rule = gcp.compute.GlobalForwardingRule("forwarding-rule",
                                                   target=target_https_proxy.self_link,
                                                   port_range="443",  # Standard HTTPS port
                                                   ip_protocol="TCP")

# Export the IP address of the Forwarding Rule to know where to send requests
pulumi.export('forwarding_rule_ip', forwarding_rule.ip_address)
```

In this Pulumi program, four main resources are used to setup SSL termination:

1. `ManagedSslCertificate`: Represents a Google-managed SSL certificate resource for your domain, used by the HTTPS Proxy to offer SSL termination.
2. `TargetHttpsProxy`: A resource that uses the SSL certificate to terminate the SSL connection and route requests to the appropriate backend service based on the specified URL map.
3. `UrlMap`: Defines the rules that route requests to different backend services based on the path in the URL.
4. `GlobalForwardingRule`: Directs incoming requests to the Target HTTPS Proxy; the resource specifies the IP address and port on which the proxy accepts requests.

After deploying this program with Pulumi, you will have SSL termination set up for your AI API, with the ability to easily manage and rotate SSL certificates through the Managed SSL Certificate service, and distribute traffic to your backend(s) with the Target HTTPS Proxy.