1. Latency-Sensitive AI Workload Routing with Azure Private DNS A Records


    To set up latency-sensitive AI workload routing with Azure Private DNS A records, we want a configuration that ensures that AI workloads are routed efficiently, with DNS resolving to the closest or most optimal endpoint based on latency.

    To accomplish this goal, we use the Azure Private DNS which allows us to manage domain names within a virtual network without needing to add a custom DNS solution. This service is useful when you need to use your own domain names to access resources within Azure.

    The key resources involved in this setup will be:

    1. PrivateDnsZone: This represents the DNS zone that we control within Azure, where we can manage our DNS records.
    2. RecordSet: A set of DNS records within the DNS zone. For A records, this is where we specify the IP addresses for our workloads.

    Here is how you might implement this in a Pulumi program written in Python:

    import pulumi import pulumi_azure_native as azure_native # Configure the resource group where we will put our resources. # This is like a folder that will contain all the things we create. resource_group = azure_private_dns_zone_group.ResourceGroup("resource_group") # Create a Private DNS Zone. # This DNS zone is private, meaning its records are only resolvable within the Azure network. private_dns_zone = azure_native.network.PrivateZone( "private_dns_zone", resource_group_name=resource_group.name, location="Global", # Azure Private DNS Zones are always Global. private_zone_name="aiworkloads.local" ) # Here we are going to create two A Records for our AI workload servers. # The servers are in different locations, and we want requests to route to the server with the best latency. # A Record for Server 1 (assume this server is in US East) a_record_set_1 = azure_native.network.RecordSet( "aRecordSet1", resource_group_name=resource_group.name, zone_name=private_dns_zone.name, ttl=300, # Time-to-live for the DNS record record_type="A", relative_record_set_name="server1", a_records=[azure_native.network.ARecordArgs(ipv4_address="")] ) # A Record for Server 2 (assume this server is in Europe West) a_record_set_2 = azure_native.network.RecordSet( "aRecordSet2", resource_group_name=resource_group.name, zone_name=private_dns_zone.name, ttl=300, # Time-to-live for the DNS record record_type="A", relative_record_set_name="server2", a_records=[azure_native.network.ARecordArgs(ipv4_address="")] ) # Now, we've got our DNS setup. Here's how it routes: # Requests to "server1.aiworkloads.local" go to (US East Server). # Requests to "server2.aiworkloads.local" go to (Europe West Server). # Export the DNS zone name so we can see the result once Pulumi is done deploying pulumi.export("private_dns_zone_name", private_dns_zone.name)

    In this Pulumi program, we first create a resource group to hold our resources. Think of it as creating a folder for your project in a file system. Then we create a Private DNS Zone in that resource group, which acts as the domain under which we can create DNS records.

    We then create two A Record Sets with different IP addresses simulating two AI workload servers located in different regions, allowing for region-based routing. The ttl (time-to-live) value is set to 300 seconds, but you can adjust this based on how often the IPs of your workloads change.

    This is a very simple example, but in a production setting, you might have traffic manager or load balancers in front of your workloads, and your DNS records would point to those instead of directly to virtual machines.

    Once the Pulumi program is run, it will set up the resources in Azure, and you'll be able to route requests through your custom domain within the Azure network. This setup can contribute to lower latency for AI workloads since traffic can be routed to the closest server based on DNS resolution.