1. EC2 Auto Scaling for Inference Workload Management


    Auto Scaling in Amazon EC2 is used to ensure you have the correct number of EC2 instances available to handle the load for your application. It can automatically increase the number of EC2 instances during demand spikes to maintain performance and decrease capacity during lulls to reduce costs. This is particularly useful for managing inference workloads, which can be spiky or unpredictable in terms of computational demand.

    I will guide you through the process of setting up an EC2 Auto Scaling group with a scaling policy by using Pulumi and AWS. We'll create an Auto Scaling group that adjusts the number of EC2 instances automatically based on the average CPU utilization.

    Here's what we'll do in our Pulumi program:

    1. Create a Launch Configuration which defines the instance type and the AMI (Amazon Machine Image) to be used for the EC2 instances in the Auto Scaling group.
    2. Define the Auto Scaling group which specifies the desired, min, and max number of instances, as well as associates it with the launch configuration.
    3. Attach a scaling policy to the Auto Scaling group which will trigger scaling actions based on the average CPU utilization metric.

    Now, let's write the program:

    import pulumi import pulumi_aws as aws # Create a Launch Configuration: this is like a blueprint for your EC2 instances that the Auto Scaling group will manage. launch_config = aws.ec2.LaunchConfiguration("app-launch-config", image_id="ami-0c55b159cbfafe1f0", # This is an example AMI ID for Amazon Linux 2; replace with your desired AMI instance_type="t2.micro", # Your preferred instance type; modify as needed name_prefix="app-lc-" # This generates unique names beginning with this prefix for your launch configurations ) # Define the Auto Scaling Group with the created launch configuration autoscaling_group = aws.autoscaling.Group("app-autoscaling-group", launch_configuration=launch_config.id, min_size=1, # Minimum number of instances in the group max_size=3, # Maximum number of instances in the group vpc_zone_identifiers=["subnet-049df61146adb8a3d"], # Replace with your VPC subnet IDs desired_capacity=1, # The desired number of instances at the creation of the group tags={ "Name": "managed-instance" } # Tags for instances launched in the Auto Scaling group ) # Scaling Policy: increases or decreases the number of EC2 instances automatically, based on the specified conditions. scaling_policy = aws.autoscaling.Policy("cpu-utilization-scaling-policy", autoscaling_group_name=autoscaling_group.name, adjustment_type="ChangeInCapacity", scaling_adjustment=1, # This will add or remove 1 instance cooldown=300, # Time, in seconds, after a scaling activity completes before another can begin policy_type="TargetTrackingScaling", estimated_instance_warmup=300, # Time to wait for a new instance to warm up (optional) target_tracking_configuration=aws.autoscaling.PolicyTargetTrackingConfigurationArgs( target_value=50.0, # The target value for the metric (CPU utilization) predefined_metric_specification=aws.autoscaling.PolicyPredefinedMetricSpecificationArgs( predefined_metric_type="ASGAverageCPUUtilization" # Tracks average CPU utilization ), ), ) # Export the names and ARNs of the resources pulumi.export("launch_configuration_name", launch_config.name) pulumi.export("autoscaling_group_name", autoscaling_group.name) pulumi.export("scaling_policy_arn", scaling_policy.arn)

    When you run this Pulumi program, it will create the necessary infrastructure on AWS for your inference workload, with automated scaling according to CPU usage. Make sure your Pulumi and AWS configurations are set correctly. You would run pulumi up to preview and deploy these resources.

    Remember to replace ami-0c55b159cbfafe1f0 with the AMI that's suitable for your workload and subnet-049df61146adb8a3d with your subnet ID(s). These values are specific to your AWS configuration and requirements.

    Also, it's important to understand this scaling policy will alter the number of instances in steps of 1 as the policy triggers. You might need to adjust the scaling_adjustment according to the workload.

    Finally, please be sure to review AWS' charges for the various services used in this program as they might incur costs on your AWS bill.