1. Load Balancer Rules for A/B Testing AI Models


    To conduct A/B testing of AI models using load balancers, you can create load balancing rules that direct a percentage of traffic to one AI model's server group (version A) and the rest to another group (version B). This can be achieved with various cloud providers, but for this example, we'll use AWS Application Load Balancer (ALB) along with listener rules to implement a basic A/B testing scenario.

    In Pulumi, we can define infrastructure as code using Python. Below, you'll see a Pulumi program that creates an AWS ALB, sets up two Target Groups (for AI model versions A and B), and defines Listener Rules to route traffic based on weights assigned to each target group:

    • AWS ALB: This is the Application Load Balancer that receives incoming traffic and distributes it according to the defined rules. It operates at Layer 7 of the OSI model, which allows for routing decisions based on content (e.g., HTTP headers, URI, etc.).
    • Target Groups: These are logical groups of servers that receive traffic from the ALB. We'll create two target groups representing the two versions of the AI model.
    • Listener Rules: These are conditions and actions that the ALB uses to route incoming requests. For A/B testing, we'll define weighted forwarding rules sending a specified percentage of requests to each target group.

    Here is the code with the detailed explanation:

    import pulumi import pulumi_aws as aws # Create an Application Load Balancer alb = aws.lb.LoadBalancer("ai-model-alb", internal=False, load_balancer_type="application", security_groups=["YOUR_SECURITY_GROUP_ID"], subnets=["YOUR_SUBNET_ID_1", "YOUR_SUBNET_ID_2"] # Specify your subnet IDs ) # Define Target Group A for AI Model Version A tg_a = aws.lb.TargetGroup("tg-a", port=80, protocol="HTTP", vpc_id="YOUR_VPC_ID" # Specify your VPC ID ) # Define Target Group B for AI Model Version B tg_b = aws.lb.TargetGroup("tg-b", port=80, protocol="HTTP", vpc_id="YOUR_VPC_ID" # Specify your VPC ID ) # Create a Listener with a default rule to forward to Target Group A listener = aws.lb.Listener("listener", load_balancer_arn=alb.arn, port=80, default_actions=[{ "type": "forward", "target_group_arn": tg_a.arn }] ) # Add a rule for A/B testing with weighted target groups ab_testing_rule = aws.lb.ListenerRule("ab-testing-rule", listener_arn=listener.arn, priority=100, actions=[{ "type": "forward", "forward": { "target_groups": [ {"arn": tg_a.arn, "weight": 80}, # 80% of the traffic goes to AI model version A {"arn": tg_b.arn, "weight": 20} # 20% of the traffic goes to AI model version B ], "stickiness": {"enabled": False}, }, }], conditions=[{ "path_pattern": { "values": ["/abtest"] } }] ) # Export the DNS name of the Load Balancer to be used as the entry point pulumi.export("load_balancer_dns", alb.dns_name)

    This program defines a load balancer (alb), two target groups (tg_a and tg_b), and a listener (listener). The rule ab_testing_rule specifies that the /abtest path will trigger A/B testing behavior, with 80% of traffic going to version A and 20% to version B of the AI model.

    You can adjust the weights according to your A/B testing requirements or split traffic evenly by setting them both to 50. Please replace YOUR_SECURITY_GROUP_ID, YOUR_SUBNET_ID_1, YOUR_SUBNET_ID_2, and YOUR_VPC_ID with your actual AWS resource IDs. If you want to test based on other conditions or path patterns, modify the conditions section of the ab_testing_rule accordingly.

    Note: Pagination or other advanced scenarios might require more complex routing conditions and additional AWS resources, which you can manage and scale with Pulumi as your testing needs evolve.