Intelligent Traffic Management for AI Model Serving with Istio

Question

Pulumi · Accepted Answer

Intelligent Traffic Management is essential for orchestrating how requests are routed to various versions of a service, especially in scenarios involving AI model serving where models could be updated frequently or A/B testing is required. Istio is a service mesh that provides a way to control traffic at a granular level, offering features like canary releases, dark launches, and service resiliency.

In a Kubernetes environment, Istio integrates directly with the Kubernetes Service objects, adding rich traffic management capabilities. Here's a Pulumi program written in Python that demonstrates how you might set up traffic routing for AI model serving using Istio.

The following program assumes that you have a Kubernetes cluster with Istio installed and that you're serving your AI models within Kubernetes pods. We're using Pulumi with the Kubernetes provider, which allows us to define our infrastructure as code in a Python program that Pulumi can deploy.

### Detailed Explanation

First, we'll create a namespace for our AI-serving application and then define the Kubernetes services for two versions of our AI model. Next, we'll create an Istio `VirtualService` to manage the traffic between these versions. In this example, we'll send 90% of the traffic to `v1` of our AI model and 10% to `v2`, simulating a canary deployment.

We'll define a `DestinationRule` which tells Istio how to find the different versions of the service. In this example, we're using Kubernetes labels to differentiate between the two versions. Finally, we use a `Gateway` to expose the service outside of the cluster.

Let's dive into the code.

```python
import pulumi
import pulumi_kubernetes as k8s

# Define the namespace for the AI services
ai_namespace = k8s.core.v1.Namespace(
    "ai-namespace",
    metadata={
        "name": "ai-services",
    }
)

# Define two Kubernetes Services for different versions of the AI model
model_service_v1 = k8s.core.v1.Service(
    "ai-model-v1",
    metadata={
        "namespace": ai_namespace.metadata["name"],
        "name": "ai-model-v1",
        "labels": {
            "version": "v1",  # Label to identify the version of the service
        },
    },
    spec={
        "selector": {
            "app": "ai-model",
            "version": "v1",
        },
        # ...
    }
)

model_service_v2 = k8s.core.v1.Service(
    "ai-model-v2",
    metadata={
        "namespace": ai_namespace.metadata["name"],
        "name": "ai-model-v2",
        "labels": {
            "version": "v2",  # Label to identify the version of the service
        },
    },
    spec={
        "selector": {
            "app": "ai-model",
            "version": "v2",
        },
        # ...
    }
)

# Create an Istio VirtualService to handle traffic management
virtual_service = k8s.apiextensions.CustomResource(
    "ai-model-virtual-service",
    api_version="networking.istio.io/v1alpha3",
    kind="VirtualService",
    metadata={
        "namespace": ai_namespace.metadata["name"],
        "name": "ai-model",
    },
    spec={
        "hosts": [
            "ai-model"  # The service DNS name
        ],
        "http": [{
            "route": [
                {
                    "destination": {
                        "host": "ai-model-v1",  # Routing 90% of traffic to v1
                        "subset": "v1",
                    },
                    "weight": 90,
                },
                {
                    "destination": {
                        "host": "ai-model-v2",  # Routing 10% of traffic to v2
                        "subset": "v2",
                    },
                    "weight": 10,
                },
            ]
        }],
    }
)

# Create a DestinationRule that defines how to route to the model versions
destination_rule = k8s.apiextensions.CustomResource(
    "ai-model-destination-rule",
    api_version="networking.istio.io/v1alpha3",
    kind="DestinationRule",
    metadata={
        "namespace": ai_namespace.metadata["name"],
        "name": "ai-model",
    },
    spec={
        "host": "ai-model",
        "subsets": [
            {
                "name": "v1",
                "labels": {
                    "version": "v1"
                }
            },
            {
                "name": "v2",
                "labels": {
                    "version": "v2"
                }
            },
        ],
    }
)

# Create an Istio Gateway to handle incoming traffic
gateway = k8s.apiextensions.CustomResource(
    "ai-model-gateway",
    api_version="networking.istio.io/v1alpha3",
    kind="Gateway",
    metadata={
        "namespace": ai_namespace.metadata["name"],
        "name": "ai-model-gateway",
    },
    spec={
        "selector": {
            "istio": "ingressgateway"
        },
        "servers": [{
            "port": {
                "number": 80,
                "name": "http",
                "protocol": "HTTP",
            },
            "hosts": [
                "*"
            ],
        }],
    }
)

# Stack exports to provide details of the resources created
pulumi.export("ai_namespace", ai_namespace.metadata["name"])
pulumi.export("model_service_v1_name", model_service_v1.metadata["name"])
pulumi.export("model_service_v2_name", model_service_v2.metadata["name"])
pulumi.export("gateway_url", gateway.metadata["name"])
```

What we have in this program:

1. **Namespace**: Created to logically separate our AI services.
2. **Services**: Define stable endpoints for the pods constituting each version of the AI model.
3. **VirtualService**: Dictates how the traffic is split between the different service versions.
4. **DestinationRule**: Helps locate the different versions of the service using subsets.
5. **Gateway**: Manages external access to the services.

With traffic management in place using Istio, you can dynamically control model serving for your AI application, providing resilience and flexibility for testing and upgrading your models without downtime.