1. Load Balancing for AI Services with Azure DNS CNAME


    Load balancing across multiple instances of an AI service is a critical requirement for ensuring high availability and scalability. In Azure, you can implement load balancing for AI services at different layers, including the application layer and the DNS layer.

    Using Azure DNS with CNAME records allows you to distribute traffic across multiple AI service instances by providing a single domain name that maps to multiple service endpoints. When a DNS query is made for the domain name, Azure DNS returns a CNAME record that points to one of the service endpoints. By updating the CNAME records, you can control the distribution of traffic to your AI services.

    In this Pulumi program, we'll set up a simple DNS-based load balancing configuration for an AI service utilizing Azure DNS and CNAME records. We will use the azure-native.network.ForwardingRule resource from the Pulumi Azure Native package to define a forwarding rule that redirects DNS queries to different AI service instances. Here's a step-by-step guide on how we achieve this with Pulumi and Python:

    1. Resource Group: We'll create a new Azure Resource Group to contain our DNS resources.

    2. DNS Zone: We'll set up a DNS zone within the resource group. This will be the domain that users will access to interact with the AI services.

    3. AI Services: For simplicity, let's assume we have two instances of an AI service with IP addresses and

    4. DNS Records: We'll create two A records pointing to the IP addresses of the AI service instances. Then we'll add a CNAME record to the DNS zone that points to these A records. Users will access the AI services via the CNAME record, which Azure DNS will resolve to one of the AI service instances.

    5. Traffic Manager Profile: We'll set up a Traffic Manager profile to manage the traffic routing method.

    6. Traffic Manager Endpoints: We'll add the AI service instances as endpoints to the Traffic Manager profile.

    Please ensure you have the Azure CLI installed and configured correctly with the appropriate permissions to create these resources before running this Pulumi code.

    Below is the program that sets up DNS load balancing for AI services in Azure using Pulumi and Python:

    import pulumi import pulumi_azure_native as azure_native # Create a new resource group for our DNS zone and related artifacts resource_group = azure_native.resources.ResourceGroup("resourceGroup") # Create a DNS Zone within the resource group dns_zone = azure_native.network.DnsZone("dnsZone", resource_group_name=resource_group.name, zone_name="example-ai-service.com") # AI Service IP addresses ai_service_ips = ["", ""] # Replace with actual IPs of AI service instances # Create 'A' DNS records for each AI service instance for idx, ip in enumerate(ai_service_ips): azure_native.network.ARecord(f"aiServiceARecord{idx+1}", resource_group_name=resource_group.name, zone_name=dns_zone.name, relative_record_set_name=f"service{idx+1}", ttl=300, a_records=[azure_native.network.ARecordArgs(ipv4_address=ip)]) # Create a CNAME record pointing to the AI service's A records - to be used by clients cname_record = azure_native.network.CnameRecord("cnameRecord", resource_group_name=resource_group.name, zone_name=dns_zone.name, relative_record_set_name="ai-service", ttl=300, cname_record=azure_native.network.CnameRecordArgs(cname="service1.example-ai-service.com")) # Set up a Traffic Manager to manage the load balancing between AI service instances tm_profile = azure_native.trafficmanager.Profile("tmProfile", resource_group_name=resource_group.name, traffic_routing_method="Performance", dns_config=azure_native.trafficmanager.DnsConfigArgs( relative_name=dns_zone.name.params['zone_name']), monitor_config=azure_native.trafficmanager.MonitorConfigArgs( protocol="https", port=443, path="/status")) # Add endpoints to the Traffic Manager profile for idx, ip in enumerate(ai_service_ips): azure_native.trafficmanager.Endpoint(f"tmEndpoint{idx+1}", resource_group_name=resource_group.name, profile_name=tm_profile.name, type="Microsoft.Network/trafficManagerProfiles/externalEndpoints", endpoint_status="Enabled", target=ip, weight=1, endpoint_location="global") # Replace the location if needed # Export the DNS Zone name and the CNAME record to be used by clients pulumi.export("dns_zone_name", dns_zone.name) pulumi.export("ai_service_cname", cname_record.relative_record_set_name.apply( lambda name: f"{name}.{dns_zone.name}"))

    In this program, ai_service_cname will be the URL that clients can use to access the AI services. The Traffic Manager monitors the health and performance of the AI services and routes traffic accordingly. Each AI service instance is added as an endpoint, with an equal weight for load balancing.

    Notice how we use Pulumi's output properties (like name attributes of created resources) which represent the future state of the cloud resources and how to combine them with the .apply method to derive further values.

    After running this program using the Pulumi CLI, you will get outputs for dns_zone_name and ai_service_cname. Clients should use the ai_service_cname URL to interact with the AI services. Traffic will be automatically balanced across the available service instances.