1. Fault-Tolerant Architecture for AI Workloads Using Target Groups


    To design a fault-tolerant architecture for AI workloads, we need to take into account the scaling and resiliency of compute resources. This can be done using target groups in combination with load balancers and auto-scaling groups which can distribute the workload evenly and handle failovers seamlessly.

    In this setup, we will create the following resources using AWS services as an example:

    1. EC2 Instances: These will serve as the compute resources running your AI workloads. We will use Spot Instances for cost-efficiency, but you may opt for On-Demand instances for persistent availability.

    2. Target Group: This acts as a logical grouping of EC2 instances, and it is used by the load balancer to forward requests to registered targets.

    3. Load Balancer: This will distribute incoming traffic among the EC2 instances in the target group and ensures that if any instance fails, the load balancer will stop sending traffic to that instance and reroute it to the other healthy instances.

    4. Auto Scaling Group: To ensure that the desired count of EC2 instances is maintained and to replace any that become unhealthy or are terminated.

    Below is a Pulumi program that sets up this architecture in AWS using Python. Note that the AI-specific configurations, like machine learning frameworks and models, are not included in this infrastructure setup. Those would be part of the application configuration on your EC2 instances.

    import pulumi import pulumi_aws as aws # Create an EC2 Security Group sec_group = aws.ec2.SecurityGroup('aiSecurityGroup', description='Enable HTTP access', ingress=[ {'protocol': 'tcp', 'from_port': 80, 'to_port': 80, 'cidr_blocks': ['']}, ]) # Create a target group for HTTP 80 target_group = aws.lb.TargetGroup('aiTargetGroup', port=80, protocol='HTTP', vpc_id=vpc_id, # Replace with your VPC ID health_check={ 'path': '/', 'protocol': 'HTTP', 'healthy_threshold': 2, 'unhealthy_threshold': 2, 'timeout_seconds': 3, 'interval_seconds': 30, 'matcher': '200-299', }) # Create a load balancer to use with the target group load_balancer = aws.lb.LoadBalancer('aiLoadBalancer', security_groups=[sec_group.id], subnets=subnet_ids, # Replace with your subnet IDs load_balancer_type='application') # Attach the target group to the load balancer listener = aws.lb.Listener('aiListener', load_balancer_arn=load_balancer.arn, port=80, default_actions=[{ 'type': 'forward', 'target_group_arn': target_group.arn }]) # Define the launch configuration using an AMI for AI workloads launch_config = aws.ec2.LaunchConfiguration('aiLaunchConfig', image_id='ami-0abcdef1234567890', # Replace with the ID of a suitable AMI instance_type='t2.micro', # Choose an instance type suitable for your workload security_groups=[sec_group.id], user_data=''' #!/bin/bash echo "User data scripts to setup AI environment" ''') # User data for AI software installations # Create an Auto Scaling Group auto_scaling_group = aws.autoscaling.Group('aiAutoScalingGroup', vpc_zone_identifiers=subnet_ids, # Replace with your subnet IDs desired_capacity=2, min_size=1, max_size=3, health_check_type='ELB', health_check_grace_period=300, force_delete=True, launch_configuration=launch_config.id, target_group_arns=[target_group.arn]) # Export the DNS name of the load balancer to access the application pulumi.export('load_balancer_dns_name', load_balancer.dns_name)

    In this program:

    • We establish a security group (sec_group) to allow HTTP traffic on port 80 to our instances.
    • We define a target group (target_group) which specifies a health check on HTTP path / with a matcher range of 200-299. This ensures that only healthy instances receive traffic.
    • We create an application load balancer (load_balancer) to distribute incoming HTTP traffic across multiple instances and specify an HTTP listener (listener) to forward traffic to the target group.
    • A launch configuration (launch_config) is defined with a placeholder AMI ID and user data script to install necessary software for the AI workload. This configuration will be used by the Auto Scaling group.
    • We define an Auto Scaling group (auto_scaling_group) to ensure a desired amount of EC2 instances are always running. It's linked to our target group to ensure new instances are automatically registered with the load balancer.
    • Finally, we export the load balancer's DNS name, so we know where to send traffic to access the AI application.

    You must replace placeholders such as ami-0abcdef1234567890, vpc_id, and subnet_ids with actual values from your AWS environment.

    This setup gives you fault tolerance because if an instance becomes unhealthy, the load balancer stops sending traffic to it and the Auto Scaling group provisions a new instance to replace it. The target group ensures that only healthy instances are used to serve your AI workloads.