Auto-Scaling Inference Endpoints with Elastic Load Balancing

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Pulumi · Accepted Answer

When you're working with cloud resources to handle varying loads, auto-scaling is an essential feature. It allows your infrastructure to automatically adjust the number of active instances based on the current demand. This capability ensures that your application remains responsive during traffic spikes and cost-efficient during periods of low activity.

For inference endpoints, where the system may need to handle a high volume of requests for data processing or machine learning inference, an auto-scaling group can work together with a load balancer to distribute the workload across multiple instances. This improves overall system robustness and helps maintain quick response times.

Elastic Load Balancing (ELB) is a service that automatically distributes incoming application traffic across multiple targets, such as EC2 instances. It can handle the varying load of your application traffic in a single Availability Zone or across multiple Availability Zones.

In Pulumi, you'd typically use the `awsx.lb.ApplicationLoadBalancer` from the `pulumi_awsx` module to set up an Application Load Balancer, along with the `awsx.ec2.AutoScalingGroup` to manage the scaling of EC2 instances.

Below is a Python program using Pulumi to set up an auto-scaling inference endpoint with an Application Load Balancer:

```python
import pulumi
import pulumi_aws as aws
import pulumi_awsx as awsx

# Create a new VPC or use an existing one. The auto-scaling group and load balancer need to be in the same VPC.
vpc = awsx.ec2.Vpc("custom-vpc", cidr_block="10.0.0.0/16")

# Create an Application Load Balancer to distribute incoming traffic.
alb = awsx.lb.ApplicationLoadBalancer("app-lb", vpc=vpc)

# Define the listener for the Application Load Balancer.
listener = alb.create_listener("app-listener", port=80)

# Define the Health Check for the Load Balancer to ensure targets are capable of handling requests.
health_check = aws.lb.TargetGroupHealthCheckArgs(
    port="80",
    protocol=aws.lb.Protocol.HTTP,
    path="/health", # replace with the path of your health check endpoint
)

# Define the port and protocol your service listens on.
port = 8080 # replace with your application's port
protocol = aws.lb.Protocol.HTTP # or HTTPS if SSL is enabled

# Create a target group for the load balancer to route traffic to.
target_group = listener.create_target_group("app-tg",
                                            port=port,
                                            protocol=protocol,
                                            health_check=health_check)

# Specify the launch configuration for the EC2 instances.
launch_config = awsx.ec2.AutoScalingLaunchConfiguration("app-launch-config",
    image=awsx.ec2.get_ami_id("amzn-ami-hvm-*"),
    instance_type="t2.medium", # adjust to your required instance type
    user_data="""#!/bin/bash
                # Your code to bootstrap the instance, like installing Docker and running your app container.
                """)

# Create an Auto Scaling Group that automatically adjusts the number of EC2 instances.
autoscaling_group = awsx.ec2.AutoScalingGroup("app-asg",
    vpc=vpc,
    target_groups=[target_group],
    launch_configuration=launch_config,
    min_size=1,
    max_size=10, # adjust to your application's needs
    desired_capacity=2,
    vpc_zones=vpc.public_subnet_ids)

# Export the URLs to access the service.
pulumi.export('load_balancer_url', alb.load_balancer.dns_name)
pulumi.export('load_balancer_listener', listener.listener.arn)
```

In this program, we start by creating a virtual private cloud (VPC) where our network infrastructure would sit. We then proceed to set up an Application Load Balancer within that VPC. Each load balancer needs a listener that checks for incoming connections; here, we've configured a listener on port 80. We define a target group where our backend targets (in this case, EC2 instances) will be registered, and specify a health check path (`/health`) which the load balancer uses to determine if the instances are healthy and can accept traffic.

Next, we create an AutoScaling Launch Configuration that defines the EC2 instances' setup — the Amazon Machine Image (AMI) to use, instance type, and user data script. The user data script is where you'd typically run setup commands for your instances such as installing dependencies and your application. Make sure to replace the example user data with your actual startup script.

Then we create an Auto Scaling Group that uses this launch configuration and automatically manages the number of EC2 instances based on defined criteria. Here, we set it to maintain between 1 and 10 instances with a desired capacity of 2 to start with.

Finally, we export the DNS name of the load balancer and the ARN of the listener. These exports can be used to integrate with other parts of your infrastructure or for easy access in testing and front-end configurations.

This setup is a fundamental starting point. In a realistic scenario, you'll want to consider securing the load balancer with SSL, restricting access to the instances, and setting up more granular scaling policies based on metrics relevant to your application's performance.