1. Auto-Scalable EC2 Instances for Real-time AI APIs


    To set up an auto-scalable group of EC2 instances on AWS for hosting real-time AI APIs, we will use the AWS Auto Scaling Group resource from Pulumi's AWS package. An Auto Scaling group will help with automatically adjusting the number of EC2 instances in response to the load on your application, ensuring that you have sufficient capacity to serve your API requests without over-provisioning resources.

    Here's how we can achieve that:

    1. Create a Launch Configuration that specifies the EC2 instance details such as the instance type, the AMI ID, and other configurations like security groups. This configuration serves as a template for the Auto Scaling group to launch EC2 instances.

    2. Create an Auto Scaling Group using the previously defined launch configuration. We define minimum and maximum numbers of instances, desired capacity, and availability zones for the group. The group manages the provisioning of EC2 instances based on the scaling policies.

    3. Define Scaling Policies to determine when to scale in or out. AWS supports various types of policies, such as target tracking scaling policies, which adjust the number of instances based on the specified target value for a specific metric.

    4. Attach the Scaling Policies to the Auto Scaling group to put them into effect.

    5. (Optional) Use Elastic Load Balancing (ELB) to distribute incoming API requests across the multiple instances of our application. This step is optional but recommended as it can increase the fault tolerance of our application.

    Let's write the Pulumi code that creates this setup:

    import pulumi import pulumi_aws as aws # Step 1: Create a Launch Configuration. launch_config = aws.ec2.LaunchConfiguration("my-launch-config", instance_type="t2.micro", # This should be chosen based on your application requirements. image_id="ami-0c55b159cbfafe1f0", # Replace with the correct AMI ID for your application. security_groups=["sg-xxxxxxxx"]) # Replace with your security group ID. # Step 2: Create an Auto Scaling Group. scaling_group = aws.autoscaling.Group("my-auto-scaling-group", max_size=5, # Maximum number of instances to scale out to. min_size=1, # Minimum number of instances to maintain. health_check_type="EC2", desired_capacity=2, vpc_zone_identifiers=["subnet-xxxxxxxx", "subnet-yyyyyyyy"], # Replace with your subnet IDs. launch_configuration=launch_config.name, tags=[{"key": "Name", "value": "my-autoscaling-group", "propagate_at_launch": True}], force_delete=True, target_group_arns=["arn:aws:elasticloadbalancing:region:account-id:targetgroup/my-target-group/xxxxxxxxxxxxxx"]) # Optional: Attach to ELB if used. # Step 3 & 4: Define and Attach the Scaling Policies to the Auto Scaling Group. scaling_policy = aws.autoscaling.Policy("my-scaling-policy", adjustment_type="ChangeInCapacity", autoscaling_group_name=scaling_group.name, cooldown=300, scaling_adjustment=2) # The number by which to scale in/out, may need adjustment based on your use case. # Step 5 (Optional): If you're using an ELB, ensure it's set up to distribute traffic across your instances. # Export the Auto Scaling Group name and ARN pulumi.export("scaling_group_name", scaling_group.name) pulumi.export("scaling_group_arn", scaling_group.arn)

    In this program:

    • We are importing the necessary modules (pulumi and pulumi_aws).
    • We create a launch configuration that specifies the instance type and AMI.
    • We then create an auto-scaling group with the specifics related to size and scaling configurations. Note that you need to replace placeholder values (ami-xxxx, sg-xxxxxxx, etc.) with actual values fit for your use.
    • We define a simple scaling policy to increase or decrease the capacity based on a predefined adjustment.
    • Optionally, if you are using an Elastic Load Balancer (ELB), its ARN should be included in the target_group_arns parameter of the scaling group.

    The scaling policy here is a simple one that adjusts the capacity by a fixed amount. AWS allows for more sophisticated scaling policies, such as target tracking scaling, which automatically adjusts the capacity to maintain the specified target for a specific metric.

    Make sure to replace placeholder values with those specific to your AWS setup, such as the instance type, AMI ID, security group, subnet IDs, and target group ARN if using Elastic Load Balancing. Also, fine-tune your scaling policy to match the specific demands and metrics relevant to your real-time AI APIs.