Intelligent Traffic Routing for AI Inference Services using Route53

Question

Pulumi · Accepted Answer

Intelligent traffic routing in AWS can be set up to distribute traffic to multiple endpoints in a way that can optimize for latency, geography, and health of the endpoints. This is typically achieved using Amazon Route 53, which is a scalable cloud Domain Name System (DNS) web service. It is commonly used for domain registration, DNS routing, and health checking of services on the AWS cloud.

In the context of AI Inference Services, intelligent traffic routing may be particularly useful. For instance, if you have inference services deployed in multiple regions, you can use Route 53 to route each user's request to the region that will give them the lowest latency.

To implement intelligent traffic routing for AI Inference Services, you would typically:

1. Deploy your AI Inference Service in multiple AWS regions.
2. Create health checks for these endpoints to ensure they are up and running.
3. Configure Route 53 to use these health checks and route traffic to healthy endpoints.

The following program demonstrates how to set up Route 53 with a simple health check for an AI Inference Service assuming you already have the service running on an EC2 instance or an ECS service. Here's how you would typically write such a program in Pulumi with Python:

```python
import pulumi
import pulumi_aws as aws

# Replace these with your actual domain name and service endpoint details
domain_name = "my-ai-service.com"
ai_service_endpoint = "https://service.region.amazonaws.com/inference"

# Set up a health check for the AI inference service endpoint
# More complex checks can be created based on the requirements
# and you can even check the endpoint response for specific result
health_check = aws.route53.HealthCheck("ai-service-health-check",
    fqdn=ai_service_endpoint,
    type="HTTP",
    request_interval=30,
    failure_threshold=3,
    resource_path="/health",  # The path your service returns "200 OK" status
    measure_latency=True
)

# Create a hosted zone for the domain if it does not already exist
zone = aws.route53.Zone("ai-service-hosted-zone",
    name=domain_name
)

# Create a record set that points to the AI service endpoint,
# and associate the health check with it
record = aws.route53.Record("ai-service-record",
    zone_id=zone.zone_id,
    name=domain_name,
    type="A",  # 'A' record if you use an IP address, or 'CNAME' if you have a DNS name
    health_check_id=health_check.id,
    set_identifier="aiServiceLatencyBasedRouting",
    weighted_routing_policies=[aws.route53.RecordWeightedRoutingPolicyArgs(
        weight=100,
    )],
    ttl=300,
)

# Export the DNS name for the AI service
pulumi.export("ai_service_domain_name", zone.name_servers)
```

Here's a brief explanation of what the above program does:

- First, a Route 53 health check is created to monitor the health of the AI Inference Service endpoint. The health check will make HTTP requests to the `/health` endpoint of your service every 30 seconds. If the endpoint fails three consecutive health checks, it will be considered unhealthy.
- A new hosted zone is created for the domain that will be used for the AI Service.
- A DNS record is created in the hosted zone which routes traffic to the service endpoint. The record is associated with the previously created health check, ensuring only healthy endpoints are used for routing traffic.
- Finally, the name servers of the hosted zone are exported. You should update your domain's name servers to these values to ensure that your domain's DNS is served by Route 53.

By setting up your DNS routing this way, you can enhance the reliability of your AI Inference Service by using AWS's network for traffic routing based on the health of your service's endpoints.