1. Centralized HTTP Proxy for Distributed AI Workloads


    To create a centralized HTTP proxy for distributed AI workloads in a cloud environment, we can use Google Cloud's infrastructure, specifically the google-native.compute/v1.RegionTargetHttpProxy resource, which works in conjunction with URL maps and forwarding rules to route HTTP requests to appropriate backend services.

    Here is why these components are used:

    • RegionTargetHttpProxy: This resource allows you to create a regional HTTP load balancer, which is an entry point for HTTP traffic targeted at your AI workloads spread across multiple instances or services within a region.
    • URL Map: This defines the rules that the HTTP load balancer uses to route incoming requests to the backend services or instances based on the URL paths.
    • Forwarding Rule: This rule connects the incoming traffic to the URL map and target HTTP proxy.

    Here's how you will set up a Centralized HTTP Proxy using Pulumi with Google Cloud:

    1. Create a Backend Service: Define your backend service, which is responsible for serving the distributed AI workloads. This could be a group of instances managed by instance groups.
    2. Set up an URL Map: Define the URL map to control the traffic routing to your backend services based on the URL paths or hostnames.
    3. Create a Target HTTP Proxy: Create the proxy that uses the URL map to route traffic to the backend services.
    4. Establish Forwarding Rules: Configure forwarding rules that use the target HTTP proxy to forward the traffic from specific IP addresses and ports to the backend services.

    Let's write the code that does this. Remember to replace placeholders such as backend_service_name, project, region, and url_map_name with actual names in your Google Cloud setup.

    import pulumi import pulumi_google_native.compute.v1 as compute # Define the Backend Service with necessary configurations. # Here you would define details for your AI workloads backend, including health checks, instance groups, etc. backend_service = compute.BackendService("backend-service-name", backends=[compute.BackendArgs( group="instance-group-url" # URL of the Instance Group configured for your AI workloads )], health_checks=["health-check-url"], # URL of the health check for your backends load_balancing_scheme="EXTERNAL", project="your-gcp-project-id", region="your-gcp-region", ) # Create the URL Map to route incoming requests to different backend services based on the path. url_map = compute.UrlMap("url-map-name", default_service=backend_service.id, project="your-gcp-project-id", region="your-gcp-region", ) # Create the Region Target HTTP Proxy that references the URL Map. target_http_proxy = compute.RegionTargetHttpProxy("target-http-proxy", url_map=url_map.id, region="your-gcp-region", project="your-gcp-project-id", ) # Forwarding rule to connect external traffic to the proxy forwarding_rule = compute.ForwardingRule("forwarding-rule", ip_protocol="TCP", load_balancing_scheme="EXTERNAL", port_range="80", region="your-gcp-region", target=target_http_proxy.id, project="your-gcp-project-id", ) # Export the IP address of the forwarding rule to use as an entry point for your distributed AI workloads. pulumi.export("http_proxy_ip", forwarding_rule.ip_address)

    In order to deploy this infrastructure, replace the placeholders with values corresponding to your specific Google Cloud setup, save the code to a .py file and run it using Pulumi CLI. The final step will output the public IP address of the centralized HTTP proxy, which you can use as the entry point to your distributed AI workloads.

    Please ensure you have Google Cloud credentials and Pulumi CLI configured correctly to allow resource creation.