1. Network Traffic Management for AI Workloads on Azure.


    To manage network traffic for AI workloads on Azure, you'll want an infrastructure that smartly routes the traffic to ensure high availability and performance. A typical setup could include a Traffic Manager to handle DNS-based traffic routing, Network Manager for network control and monitoring, and Private Endpoints for secure access to services.

    In this Pulumi program, we'll create:

    1. Azure Traffic Manager Profile: This serves as the traffic routing mechanism, directing client requests to the most appropriate service endpoint based on a traffic-routing method and the health of the endpoints.

    2. Azure Network Manager: Manages and defines network control across various subscriptions and virtual networks in Azure. It offers a centralized way to manage traffic across different regions and deployments.

    3. Azure Private Endpoint: This provides a secure connection to Azure services by privatizing the endpoint within the virtual network. It's an important feature for security-sensitive AI workloads that might require isolation from the public internet.

    Let's go ahead and write a Pulumi program for setting up this infrastructure.

    import pulumi import pulumi_azure_native as azure_native # Create a Resource Group for organizing all the related resources resource_group = azure_native.resources.ResourceGroup('ai_workload_rg') # Create an Azure Traffic Manager Profile for routing incoming traffic based on various rules traffic_manager_profile = azure_native.network.TrafficManagerProfile('ai_workload_traffic_manager_profile', resource_group_name=resource_group.name, traffic_routing_method=azure_native.network.TrafficRoutingMethod.PRIORITY, dns_config=azure_native.network.TrafficManagerProfileDnsConfigArgs( relative_name='aiworkload', ttl=60 ), monitor_config=azure_native.network.TrafficManagerProfileMonitorConfigArgs( protocol=azure_native.network.MonitorProtocol.HTTPS, port=443, path='/healthcheck' ), location='global' ) # Create an Azure Network Manager for centralized network control and monitoring network_manager = azure_native.network.NetworkManager('ai_workload_network_manager', description="Network Manager for AI Workloads", resource_group_name=resource_group.name, location='global' ) # Creating a Private Endpoint for accessing Azure services securely from within the Virtual Network private_endpoint = azure_native.network.PrivateEndpoint('ai_workload_private_endpoint', resource_group_name=resource_group.name, location=resource_group.location, private_link_service_connections=[azure_native.network.PrivateLinkServiceConnectionArgs( name='aiworkloadPLSConnection', private_link_service_id= # Here you should link to your Azure AI service resource ID, group_ids=['groupId'] # Replace with correct group ID )], subnet=azure_native.network.SubnetArgs( id= # The ID of the subnet where you'll place this private endpoint ) ) # Output the FQDN of the Traffic Manager for reference pulumi.export('traffic_manager_fqdn', traffic_manager_profile.fqdn) # Output the ID of the Network Manager pulumi.export('network_manager_id', network_manager.id) # Output the ID of the Private Endpoint for reference pulumi.export('private_endpoint_id', private_endpoint.id)

    In the above program:

    • We create a resource group to house all of the components we need for traffic management.
    • A Traffic Manager profile is set up with DNS configuration and a health monitoring path. Here we use priority-based routing; however, Traffic Manager supports other methods like performance, weighted, or geographic.
    • The Network Manager is initialized but not connected to any specific network resources. To manage traffic effectively, it needs to be associated with one or more virtual networks or subscriptions.
    • Private Endpoint securely connects to Azure services from within your virtual network. Note that you'll need to specify the service you're connecting to by providing its resource ID (private_link_service_id) and the corresponding subnet ID where the private endpoint will reside.

    This setup is a starting point for managing network traffic in a robust and secure manner for AI workloads on Azure. You may need to tailor the traffic rules, network configurations, and private connections based on your workload's unique requirements. After deployment, monitoring and adjustments may be necessary as your AI workloads scale or change.