Load Balancing for AI Inference Services

Question

Pulumi · Accepted Answer

Load balancing is an essential component for deploying scalable and highly available AI inference services. It helps distribute incoming inference requests across multiple service instances, ensuring that no single instance becomes a bottleneck leading to potential latency or dowtime.

In a cloud environment, you would typically deploy your AI inference service on a set of compute instances (like EC2 instances in AWS, or VM instances in GCP or Azure). Then, you would place a load balancer in front of these instances to evenly distribute the traffic. The load balancer would also monitor the health of your instances and stop sending traffic to any that become unhealthy.

Here's how you could set up a basic load balancing scenario for AI inference services using Pulumi and AWS. Our program will create a Target Group, an Application Load Balancer (ALB) and a Listener. The Target Group maintains a list of service instances, the ALB distributes incoming requests, and the Listener checks for requests on a specified port and forwards them to the Target Group.

Let's walk through the Pulumi program that sets this up:

1. **AWS Target Group**: A target group is used to route requests to one or more registered targets, such as EC2 instances. When creating a target group, you specify the protocol and port number for incoming traffic, and a health check setting that the load balancer uses to determine if the target is healthy.

2. **AWS Application Load Balancer (ALB)**: The ALB services HTTP and HTTPS traffic. It improves the availability and scalability of your application by distributing incoming application traffic across multiple targets, such as EC2 instances.

3. **AWS Listener**: A listener checks for connection requests from clients, using the protocol and port that you configure, and forwards requests to one or more target groups based on the content of the request.

Below is the Python program using Pulumi for setting up load balancing for AI inference services.

```python
import pulumi
import pulumi_aws as aws

# Define the AWS region that the resources will be created in
aws_region = "us-west-2"
pulumi.config.set("aws:region", aws_region)

# Create an Application Load Balancer (ALB)
alb = aws.lb.LoadBalancer("ai-inference-lb",
    internal=False,
    load_balancer_type="application", # Use an "application" load balancer
    subnets=["subnet-XXXXXXX", "subnet-YYYYYYY"], # Specify the subnets the ALB is associated with
)

# Define a target group for the ALB to direct traffic to
target_group = aws.lb.TargetGroup("ai-inference-tg",
    port=80,
    protocol="HTTP",
    vpc_id="vpc-XXXXXXX",  # Replace with the appropriate VPC ID
    health_check={
        "healthy_threshold": 2,
        "unhealthy_threshold": 2,
        "timeout": 3,
        "path": "/", # AI inference service endpoint
        "interval": 30,
    },
)

# Define a listener for the ALB that checks for incoming requests
listener = aws.lb.Listener("ai-inference-listener",
    load_balancer_arn=alb.arn,  # Reference the ALB created earlier
    port=80,
    default_actions=[{
        "type": "forward",
        "target_group_arn": target_group.arn, # Forward to our target group
    }],
)

# Export the DNS name of the ALB to access the AI inference service
pulumi.export("load_balancer_dns_name", alb.dns_name)
```

This program sets up the ALB to listen for HTTP requests on port 80. When a request comes in, it forwards that to the target group, which in turn sends it to one of the available service instances.

AI inference services can be set on EC2 instances, which should be registered with our target group. The health check will ping the inference service endpoint and ensure that it only directs traffic to healthy instances.

To use the above Pulumi program:

- Be sure to replace the `subnet-XXXXXXX`, `subnet-YYYYYYY`, and `vpc-XXXXXXX` with your actual subnet and VPC IDs.
- You may need additional configurations depending on your specific requirements, like configuring SSL, setting more complex routing rules, or adding additional listeners for different protocols.
- After code deployment, you can get the DNS name of the load balancer with the pulumi stack output command, and that is what you would use in your application to access the AI inference services.