1. Establishing Global Anycast IPs for Low Latency AI Inference Services


    To establish Global Anycast IPs that can help provide low latency for AI inference services, we generally consider using a cloud provider's global infrastructure that can route clients to the nearest data center where our AI models are deployed.

    For Google Cloud Platform (GCP), we can use a Global IP address that can be used for different services such as HTTP(S), SSL proxy, and TCP proxy load balancing, to evenly distribute the load and reduce latency by ensuring the users connect to the nearest instance of our service.

    Here is how we can do this using Pulumi with Google Cloud:

    1. Global Address: We'll create a GlobalAddress in GCP which is a globally scoped IP address we can use for our globally distributed services.

    2. Forwarding Rule: We'll create a GlobalForwardingRule which will use the Global Address and connect incoming requests to a target, such as a target HTTP proxy if we are setting up HTTP(S) load balancing.

    3. Target HTTP Proxy and URL Map: To direct traffic to multiple backend services or endpoints, we'll need a TargetHttpProxy and a UrlMap.

    4. Backend Services: These are configurations that define how our compute instances will serve traffic. The backend services will be configured with instance groups that contain the compute instances running our AI inference service.

    Below is the Pulumi program, written in Python, that sets up a global anycast IP and prepares its integration with load balancing and backend services. Please note that this code only provisions the IP and starts setting up the load balancing structure, and does not implement the actual AI services, which would be application-specific.

    import pulumi import pulumi_google_native.compute.v1 as compute # Create a global IP address global_ip = compute.GlobalAddress("global-ip", address_type="EXTERNAL", ip_version="IPV4", purpose="LOAD_BALANCING" ) # Assume you have defined the rest of the load balancing resources such as: # - Backend services to point to your AI inference servers # - Instance template and managed instance groups for your AI service # - UrlMap and TargetHttpProxy to define the routing for incoming traffic # The full load balancing setup requires: # - defining an Instance Template # - creating a Managed Instance Group based on the template # - setting up a Backend Service # - creating a URL Map that routes requests to the Backend Service # - defining a Target HTTP Proxy that uses the URL Map # - setting up a Global Forwarding Rule that uses the Target Proxy # An example of creating a forwarding rule using the IP address: # forwarding_rule = compute.GlobalForwardingRule("forwarding-rule", # ip_address=global_ip.address, # Reference the globally scoped IP address here # ip_protocol="TCP", # Typically, AI inference services will use TCP or HTTP(S) # load_balancing_scheme="EXTERNAL", # port_range="80-80", # Port range depends on the service you are running # target=...) # The identifier of the TargetHttpProxy resource # Don't forget to replace the `...` with actual target and other properties. # Once these resources are configured, traffic to the global anycast IP will be # properly directed to the nearest instance of the AI inference service, ensuring # low latency for end users. # When the global forwarding rule is ready, you can export the global IP address so you can easily reference it: pulumi.export("global_anycast_ip", global_ip.address)

    Explanation of each component:

    • GlobalAddress: We create a global IP address which is used by clients around the world to access the AI inference services. By pointing users to this single IP, the underlying GCP infrastructure can route the traffic to the nearest backend service instance based on where the request is coming from.

    • GlobalForwardingRule: This routes incoming requests from the global IP address to a specified target, such as a proxy load balancer.

    • Backend Services, TargetHttpProxy, UrlMap: These components configure the load balancer to send incoming traffic to the appropriate backend instances, allowing for intelligent routing and, if necessary, SSL offloading or content-based routing.

    • Exporting the Global IP: The export line at the end of the script makes the allocated global IP address available as an output of the Pulumi deployment, which can then be referenced as needed, perhaps in DNS configurations or for display within a CI/CD pipeline.

    This Pulumi program sets the stage for a global anycast IP configuration which you can expand with specific details of your AI inference service setup. Further configuration can include security policies, SSL certificates for HTTPS traffic, autoscaling policies for instance groups, and much more depending on your specific use case.