Auto-scaling ML Inference with GCP Global Load Balancing

Question

Pulumi · Accepted Answer

To set up an auto-scaling ML inference on Google Cloud Platform with global load balancing, we'll need to create and configure several resources. Auto-scaling will ensure that as the demand for machine learning inference grows, new instances are automatically launched to handle the increased load. Global Load Balancing allows us to balance the load of user requests across multiple instances distributed globally, providing low latency and high availability.

Here are the steps and resources we'll use:

1. **Backend Service**: This will be the backend for the load balancer. It defines how the load balancer will distribute traffic among the attached backends, which in our case will be instances doing ML inference.
2. **Instance Template**: We will define an instance template which specifies the machine type and image that will be used for ML inference.
3. **Managed Instance Group**: We will create a Managed Instance Group which uses the instance template. It enables auto-scaling and load balancing and ensures our VMs are spread across different zones for high availability.
4. **Autoscaler**: Tied to the Managed Instance Group, it automatically adjusts the number of instances in the group based on the defined utilization policy (e.g., CPU usage).
5. **Global Forwarding Rule**: To direct incoming requests to the correct backend service.
6. **Target HTTP(S) Proxy**: This will act as the point of contact for the forwarding rule. It routes incoming requests from the forwarding rule to the correct URL map based on the path of the request.

Below is a Pulumi program that sets up these resources using the Pulumi Python SDK.

```python
import pulumi
import pulumi_gcp as gcp

# Step 1: Define Backend Service
backend_service = gcp.compute.BackendService("ml-backend-service",
    backends=[{
        "group": "instance-group-uri", # Replace with the managed instance group's instance group manager's instance group
    }],
    health_checks=["health-check-uri"], # Replace with your health check URI
    # Other necessary backend service configurations would go here
)

# Step 2: Define Instance Template for ML Inference
instance_template = gcp.compute.InstanceTemplate("ml-instance-template",
    # You would define the machine type and other configs like boot disk, network interfaces etc. here
)

# Step 3: Define Managed Instance Group
managed_instance_group = gcp.compute.RegionInstanceGroupManager("ml-region-instance-group-manager",
    base_instance_name="ml-instance",
    instance_template=instance_template.id,
    target_pools=["target-pool-uri"], # Replace with the pool that will be using this instance
    target_size=1, # Could set an initial size or leave the autoscaler to handle scaling
    # Define multiple zones for high availability
    distribution_policy_zones=[
        "us-central1-a",
        "us-central1-b",
        "us-central1-c",
    ],
)

# Step 4: Define Autoscaler
autoscaler = gcp.compute.RegionAutoscaler("ml-autoscaler",
    target=managed_instance_group.id,
    autoscaling_policy={
        "max_replicas": 10,
        "min_replicas": 1,
        "cpu_utilization": {
            "target": 0.6,
        },
    },
)

# Step 5: Define Global Forwarding Rule
forwarding_rule = gcp.compute.GlobalForwardingRule("ml-forwarding-rule",
    port_range="80",
    target="target-http-proxy-uri", # Replace with your target HTTP Proxy URI
    # Other necessary global forwarding rule configurations would go here
)

# Step 6: Define Target HTTP(S) Proxy
http_proxy = gcp.compute.TargetHttpProxy("ml-target-http-proxy",
    url_map="url-map-uri", # Replace with your URL map URI
    # Other necessary proxy configurations would go here
)

# Omitting health checks, URL map and other configurations for the sake of brevity

# Exporting the IP of the Global Forwarding Rule so you can access the ML inference service
pulumi.export("ml_service_ip", forwarding_rule.ip_address)
```

This program defines the resources necessary for a basic auto-scaling setup on GCP. Here’s what we do with each step:

- **Step 1**: We configured a backend service with our own group and health check URI.
- **Step 2**: An instance template is defined which stipulates the requirements of each VM instance.
- **Step 3**: We created a Managed Instance Group Manager that will use the instance template and specified it to be spread across three zones.
- **Step 4**: The autoscaler policy is defined to maintain CPU utilization at 60%, with a minimum of 1 and a maximum of 10 replicas.
- **Step 5**: We set up a global forwarding rule to listen on port 80 and forward traffic to our proxy.
- **Step 6**: The Target HTTP Proxy is the glue between the global forwarding rule and the backend service URL map.

Please note that for simplicity, I've omitted some of the detailed configurations needed for each resource, such as networking configurations, health checks, and the instance template details. Also, the URIs (e.g., "instance-group-uri", "health-check-uri") must be replaced with actual resource URIs or references to the Pulumi outputs for those resources.

In a real-world scenario, additional configurations may be necessary to fully flesh out the auto-scaling capabilities and to meet specific security, networking, and deployment requirements for your ML inference workload.