Auto-scaling ML Model Serving Workloads with App Mesh

Question

Pulumi · Accepted Answer

Auto-scaling is a critical feature for machine learning (ML) workloads, allowing you to handle variable load efficiently. When implementing auto-scaling for ML model serving with AWS, you can utilize multiple services to set up a robust and responsive environment. AWS App Mesh is particularly useful as it allows you to control and monitor microservices without requiring changes to the application code.

AWS App Mesh is a service mesh that provides application-level networking, making it easy to manage microservice architectures. It standardizes how your microservices communicate, giving you end-to-end visibility and ensuring high-availability.

To set up auto-scaling for ML workloads, you can use Kubernetes along with AWS App Mesh. Kubernetes provides native support for auto-scaling through the Horizontal Pod Autoscaler (HPA), which automatically scales the number of pods in a deployment based on observed CPU utilization or custom metrics.

In this program, we'll define the infrastructure needed for auto-scaling ML model serving workloads using Pulumi with AWS App Mesh and Kubernetes.

Here are the steps we will follow in the program:
1. Set up an AWS App Mesh environment, including a mesh, virtual nodes, virtual services, and a virtual router.
2. Deploy a Kubernetes cluster and configure the Horizontal Pod Autoscaler (HPA).
3. Configure the HPA to scale based on custom metrics appropriate for ML workloads (e.g., request latency, queue depth, etc.).

```python
import pulumi
import pulumi_aws as aws
import pulumi_kubernetes as k8s
from pulumi_kubernetes.apps.v1 import Deployment
from pulumi_kubernetes.autoscaling.v2beta1 import HorizontalPodAutoscaler

# Initialize AWS provider configuration.
aws_provider = aws.Provider('aws', region='us-west-2')

# Create an AWS App Mesh Mesh resource. This acts as the logical boundary for the network traffic
# between the services that reside within it.
app_mesh = aws.appmesh.Mesh('appMesh', opts=pulumi.ResourceOptions(provider=aws_provider))

# Define the Virtual Node(s) for the ML workloads. A Virtual Node acts as a logical pointer to a
# particular service.
virtual_node = aws.appmesh.VirtualNode(
    'virtualNode',
    mesh_name=app_mesh.name,
    spec=aws.appmesh.VirtualNodeSpecArgs(
        # Definition for the service discovery mechanism for the virtual node.
        service_discovery=aws.appmesh.VirtualNodeSpecServiceDiscoveryArgs(
            dns=aws.appmesh.VirtualNodeSpecServiceDiscoveryDnsArgs(
                hostname='your-ml-service.local'
            )
        ),
    ),
    opts=pulumi.ResourceOptions(provider=aws_provider)
)

# Define a Virtual Service, which is an abstraction of a real service provided by a virtual node.
virtual_service = aws.appmesh.VirtualService(
    'virtualService',
    mesh_name=app_mesh.name,
    spec=aws.appmesh.VirtualServiceSpecArgs(
        provider=aws.appmesh.VirtualServiceSpecProviderArgs(
            virtual_node=aws.appmesh.VirtualServiceSpecProviderVirtualNodeArgs(
                virtual_node_name=virtual_node.name
            )
        )
    ),
    opts=pulumi.ResourceOptions(provider=aws_provider)
)

# Deploy a Kubernetes cluster. For demonstration purposes, we are going to use a mocked cluster.
# In a real-world scenario, you would configure your Kubernetes cluster here.
k8s_cluster = [...] # Your Kubernetes cluster configuration.

# Deploy the ML service as a Kubernetes Deployment.
ml_deployment = Deployment(
    'mlDeployment',
    spec={
        'selector': {'matchLabels': {'app': 'ml-service'}},
        'replicas': 1,
        'template': {
            'metadata': {'labels': {'app': 'ml-service'}},
            'spec': {
                'containers': [{
                    'name': 'ml-container',
                    'image': 'your-ml-model-image:latest',   # Replace with your ML model serving image.
                    'ports': [{'containerPort': 8080}]
                }]
            }
        }
    }
)

# Create a Horizontal Pod Autoscaler to automatically scale the number of pods
# in the ML service deployment based on observed CPU utilization.
hpa = HorizontalPodAutoscaler(
    'mlHpa',
    spec={
        'scaleTargetRef': {
            'apiVersion': 'apps/v1',
            'kind': 'Deployment',
            'name': ml_deployment.metadata['name']
        },
        # Add custom metrics for your ML model serving here.
        'minReplicas': 1,
        'maxReplicas': 10,
        'metrics': [{
            'type': 'Resource',
            'resource': {
                'name': 'cpu',
                'target': {
                    'type': 'Utilization',
                    'averageUtilization': 80
                }
            }
        }]
    }
)

# Export the App Mesh Virtual Node name and endpoint for the ML service.
pulumi.export('virtual_node_name', virtual_node.name)
pulumi.export('ml_service_endpoint', f"http://{virtual_node.spec.service_discovery.dns.hostname}")
```

In this program, you're defining:
- An App Mesh mesh as a network boundary.
- A virtual node which represents a backend service.
- A virtual service that abstracts the actual backend service behind a virtual node.
- A Kubernetes deployment for deploying your ML model as a microservice.
- A Horizontal Pod Autoscaler to scale your service based on CPU utilization or custom metrics.

Remember to replace the values for `mesh_name`, `hostname`, and `image` with your specific details.

Please make sure AWS CLI and Pulumi are properly configured to interact with your AWS account and the Kubernetes cluster. Additionally, replace the placeholder `[...]` with your actual Kubernetes cluster configuration depending on your environment setup.