Load Balancing for Distributed AI Services

Question

Pulumi · Accepted Answer

Load balancing is crucial when you're running distributed AI services because it helps to distribute incoming network traffic across multiple servers or services. It ensures that no single server bears too much demand. By spreading the load, it improves responsiveness and increases the availability of your applications.

We will be focusing on Google Cloud Platform (GCP) for this example. We'll use Google Cloud Load Balancer by configuring a Backend Service with a managed instance group as backends. The managed instance group will allow us to auto-scale our AI services based on demand. We'll use an HTTP(S) Load Balancing since it's a common choice for layer 7 load balancing.

Here's how you can use Pulumi to set up load balancing for distributed AI services in GCP:

1. Define a managed instance group that will run your AI services.
2. Set up a Backend Service that will define how the Load Balancer will distribute traffic among the instances.
3. Create a URL Map to define the rules that will guide HTTP(S) requests to the appropriate backend services.
4. Configure a Target HTTP Proxy to use the URL Map.
5. Set up a Forwarding Rule to direct incoming traffic to the HTTP Proxy, which in turn will use the Backend Service to distribute traffic to the appropriate instances.

Let's put this into a Pulumi program. Below is a Python Pulumi program that shows how you would write the infrastructure as code to accomplish this setup:

```python
import pulumi
import pulumi_gcp as gcp

# Create a managed instance group for our AI services. The template defines the
# configuration of the instances in the managed group.
instance_template = gcp.compute.InstanceTemplate("ai-instance-template",
    machine_type="n1-standard-1",
    tags=["ai-service"],
    disk=[
        {
            "boot": True,
            "auto_delete": True,
            "source_image": "projects/debian-cloud/global/images/family/debian-10",
        },
    ],
    network_interfaces=[
        {
            "network": "default",
            "access_configs": [{}],  # This requests an ephemeral IP address.
        },
    ],
    service_accounts=[{
        "email": "default",
        "scopes": ["https://www.googleapis.com/auth/cloud-platform"],
    }],
)

# Create a managed instance group using the instance template. This will let us auto-scale
# and manage instances running our AI services.
instance_group_manager = gcp.compute.InstanceGroupManager("ai-instance-group",
    instance_template=instance_template.self_link,
    base_instance_name="ai-instance",
    target_size=3,  # Start with 3 instances
    zone="us-central1-a",
)

# Create a health check to determine if instances are responsive and to decide 
# if traffic should be sent to that instance.
health_check = gcp.compute.HealthCheck("ai-service-health-check",
    check_interval_sec=5,
    timeout_sec=5,
    tcp_health_check={"port": 80},
)

# Define a backend service that uses the health check.
backend_service = gcp.compute.BackendService("ai-backend-service",
    backends=[{
        "group": instance_group_manager.instance_group,
    }],
    health_checks=[health_check.self_link],
    port_name="http",
    protocol="HTTP",
)

# Set up a URL Map to define the rules that guide HTTP(S) requests.
url_map = gcp.compute.URLMap("url-map",
    default_service=backend_service.self_link,
)

# Create a target HTTP proxy to route requests to our URL map.
target_http_proxy = gcp.compute.TargetHttpProxy("target-proxy",
    url_map=url_map.self_link,
)

# Create and configure a global forwarding rule to handle and route incoming requests.
forwarding_rule = gcp.compute.GlobalForwardingRule("forwarding-rule",
    port_range="80",
    target=target_http_proxy.self_link,
)

# Export the external IP address of the Load Balancer.
pulumi.export('load_balancer_ip', forwarding_rule.ip_address)
```

In this Pulumi program:

- We start by creating an instance template. This template describes the VM instances that will be created and managed by the instance group manager.
- We then create a managed instance group with the `InstanceGroupManager` resource. This group will maintain the desired number of instances and apply the instance template to each of them.
- A health check is created to ensure only healthy instances receive traffic.
- The `BackendService` serves as the backend for the load balancer with instances from the managed group as its backends and the earlier-defined health check.
- The `URLMap` resource sets up rules on how incoming requests should be routed to your backend services.
- Setting up a `TargetHttpProxy` and connecting it with our `URLMap` allows us to use layer 7 load balancing features like URL-based routing.
- The `GlobalForwardingRule` sets up a global IP and listens on a specific port, directing traffic to our `TargetHttpProxy`.
- Finally, we export the IP address of our load balancer so we can easily find the entry point for our AI services.

With this setup, you can ensure your AI services remain highly available and can scale based on user demand. This Pulumi code will provision the infrastructure needed on GCP. To use this Pulumi code, you would need to have your GCP credentials configured for Pulumi.