Content-Based Routing for AI-Powered Web Applications

Question

Pulumi · Accepted Answer

Content-Based Routing (CBR) is a technique used to route user requests to different servers based on the content of the request. It is commonly used in load balancing to distribute traffic across multiple servers, ensuring that each request is handled efficiently and effectively.

For AI-powered web applications, CBR can be particularly useful for routing specific types of requests to specialized servers. For instance, an AI application that handles both text and image processing might route text-related requests to servers optimized for natural language processing, while image-related requests go to servers that are set up for image recognition tasks.

To implement content-based routing, you would typically set up a load balancer that can inspect the content of incoming requests and make routing decisions based on predefined rules. The load balancers provided by cloud providers like AWS, GCP, and Azure offer built-in capabilities for CBR.

Let's create a simple Pulumi program in Python that sets up an AWS Application Load Balancer (ALB) to route traffic based on URL path. We’ll define two listener rules that forward requests to different target groups based on the requested URL path.

In this example, we assume you have two target groups:
- `textProcessingTargetGroup` for handling `/text` path which routes to the servers optimized for text processing.
- `imageProcessingTargetGroup` for handling `/image` path which routes to the servers optimized for image processing.

Here's how we'll set up content-based routing for an AI-powered web application on AWS:

```python
import pulumi
import pulumi_aws as aws

# Create a new load balancer
lb = aws.lb.LoadBalancer("my-load-balancer",
    internal=False,
    load_balancer_type="application",
    security_groups=["sg-123456"],  # Replace with your security group
    subnets=["subnet-12345678", "subnet-87654321"])  # Replace with your subnets

# Define the two target groups for text and image processing
text_processing_tg = aws.lb.TargetGroup("text-processing-tg",
    port=80,
    protocol="HTTP",
    vpc_id="vpc-12345678")  # Replace with your VPC ID
image_processing_tg = aws.lb.TargetGroup("image-processing-tg",
    port=80,
    protocol="HTTP",
    vpc_id="vpc-12345678")  # Replace with your VPC ID

# Create a listener for the load balancer
listener = aws.lb.Listener("listener",
    load_balancer_arn=lb.arn,
    port=80,
    default_actions=[{
        "type": "fixed-response",
        "fixed_response": {
            "content_type": "text/plain",
            "message_body": "404: Not Found",
            "status_code": "404"
        }
    }])

# Create listener rule for text processing path
text_rule = aws.lb.ListenerRule("text-rule",
    actions=[{
        "type": "forward",
        "target_group_arn": text_processing_tg.arn
    }],
    conditions=[{
        "path_pattern": {
            "values": ["/text/*"]
        }
    }],
    listener_arn=listener.arn,
    priority=100)

# Create listener rule for image processing path
image_rule = aws.lb.ListenerRule("image-rule",
    actions=[{
        "type": "forward",
        "target_group_arn": image_processing_tg.arn
    }],
    conditions=[{
        "path_pattern": {
            "values": ["/image/*"]
        }
    }],
    listener_arn=listener.arn,
    priority=101)

# Export the DNS name of the load balancer to be accessed by the users
pulumi.export("load_balancer_dns_name", lb.dns_name)
```

In the above program:

1. We create an Application Load Balancer that's internet-facing with the `aws.lb.LoadBalancer` resource.
2. We set up two target groups for handling different types of content with the `aws.lb.TargetGroup` resource.
3. A listener is created on the load balancer to receive incoming traffic with the `aws.lb.Listener` resource.
4. We define two listener rules with the `aws.lb.ListenerRule` resource to inspect the URL path and forward requests to the appropriate target group.
5. The default action for the listener sends a `404 Not Found` response to any traffic that doesn't match the rules. This is a simple fixed-response action.
6. Lastly, we export the DNS name of the load balancer, which will be the URL you provide to your users.

This setup routes `/text/*` paths to a server group optimized for text processing and `/image/*` paths to another group for image processing, providing content-based routing for your AI-powered application.

Remember to replace placeholders like security group IDs, subnet IDs, and VPC ID with actual values from your AWS environment.