1. Multi-Region AI Inference with GCP URL Maps


    To set up a multi-region AI Inference on Google Cloud Platform (GCP), you'll likely want to distribute your AI workloads across multiple regions and use a URL map to route traffic intelligently based on the location of the request or other factors. URL maps are used with HTTP(S) load balancers to route incoming requests to backend services or buckets.

    The following resources can be employed for this setup:

    • RegionBackendService: This defines your backend services in different regions. These are the actual services that run your AI inference models.
    • RegionUrlMap: This is the URL map that will route requests to the appropriate backend service based on rules you define, such as path matching or advanced routing features like weighted backends.
    • RegionTargetHttpsProxy: This resource references the URL map and is used by the global forwarding rule to route requests.
    • GlobalForwardingRule: The entry point for all incoming requests on a global HTTP(S) load balancer. It uses an IP address that is accessible globally.

    Here's a basic Pulumi Python program to get you started with multi-region AI inference using GCP URL Maps. This program will create the necessary URL map and backend services for two regions:

    import pulumi import pulumi_gcp as gcp # Create a global IP address for your load balancer ip_address = gcp.compute.GlobalAddress("ai-ip-address") # Backend services in each region. backend_service_us = gcp.compute.RegionBackendService("ai-backend-service-us", region="us-central1", protocol="HTTP", timeout_sec=30, health_checks=[<health_check_id>] ) backend_service_eu = gcp.compute.RegionBackendService("ai-backend-service-eu", region="europe-west1", protocol="HTTP", timeout_sec=30, health_checks=[<health_check_id>] ) # URL map to route requests to the correct backend service based on region url_map = gcp.compute.RegionUrlMap("ai-url-map", region="us-central1", default_service=backend_service_us.self_link, host_rules=[ gcp.compute.RegionUrlMapHostRuleArgs( hosts=["your-service.example.com"], path_matcher="path-matcher-name", ) ], path_matchers=[ gcp.compute.RegionUrlMapPathMatcherArgs( name="path-matcher-name", default_service=backend_service_us.self_link, path_rules=[ gcp.compute.RegionUrlMapPathMatcherPathRuleArgs( paths=["/europe/*"], service=backend_service_eu.self_link, ) ] ) ] ) # A target HTTPS proxy to use the URL map target_proxy = gcp.compute.RegionTargetHttpsProxy("ai-https-proxy", region="us-central1", url_map=url_map.self_link, ssl_certificates=[<ssl_certificate_id>] ) # A global forwarding rule to handle and route incoming requests forwarding_rule = gcp.compute.GlobalForwardingRule("ai-forwarding-rule", ip_address=ip_address.address, # Reference the global IP created above target=target_proxy.self_link, port_range="443", # Typically for HTTPS traffic ) pulumi.export("lb_ip", ip_address.address)

    Replace <health_check_id> with the ID of an appropriate health check for your backend services and <ssl_certificate_id> with the ID of a managed or self-managed SSL certificate. This is a very basic setup, and you would need to customize the RegionBackendService and RegionUrlMap resources according to your actual service URLs and load balancing requirements.

    Additionally, more sophisticated routing might include configuring:

    • Advanced load balancing features like CDN, SSL policies, or custom request/response headers.
    • Weighted load balancing if you have multiple instances of your inference service within a region and want to distribute traffic among them based on weights.
    • More regions and backend services for true global coverage.
    • More advanced health checks that ensure your AI inference services are functioning correctly before they receive traffic.
    • Security features like Cloud Armor for DDoS protection and WAF.

    For a production setup, ensure that you have proper monitoring, logging, and redundancy in place to handle failures and to scale appropriately based on the workload.