Global Load Balancing for AI Services on GKE

Question

Pulumi · Accepted Answer

Global Load Balancing on Google Kubernetes Engine (GKE) allows services to be scaled and accessed globally, improving latency and throughput by directing user traffic to the closest regional instance of the service, and can intelligently route traffic to other regions during outages. This is particularly useful for AI services that may require high availability and low latency for users around the world.

To achieve this, we would typically set up a multi-regional GKE cluster, deploy our AI services on it, and then configure a global HTTP(S) load balancer to route external HTTP(S) traffic to the appropriate regional backend service based on the user's location and the health of the service. We’ll use Google Cloud's network resources, such as Backend Services, Global Forwarding Rules, and possibly also configure Autoscalers to automatically adjust the number of instances in response to load.

Here is a Pulumi program in Python that configures a global load balancer for AI services on GKE:

```python
import pulumi
import pulumi_gcp as gcp

# Assuming that the GKE clusters and relevant AI services are already deployed,
# a Backend Service will be needed for each regional service.
# The Backend Services will manage backends consisting of instance groups with the deployed services.
ai_service_backend = gcp.compute.BackendService('ai-service-backend',
    backends=[
        gcp.compute.BackendServiceBackendArgs(
            # Assume an existing instance group URL which should be obtained dynamically based on actual deployments
            group='INSTANCE_GROUP_URL',
        ),
    ],
    health_checks=['HEALTH_CHECK_URL'],  # Again, use the URL of the relevant health check
    protocol='HTTP',  # This is based on the service requirement (HTTP/HTTPS/HTTP2)
    load_balancing_scheme='EXTERNAL',
    enable_cdn=False,  # Assuming CDN is not necessary for the AI services
)

# Create a Global Forwarding Rule to route incoming requests to the correct backend service.
global_forwarding_rule = gcp.compute.GlobalForwardingRule('ai-service-global-forwarding-rule',
    backend_service=ai_service_backend.id,
    ip_protocol='TCP',  # Traffic type expected, typically TCP for HTTP(S)
    port_range='80',  # Port range will depend on the services and if TLS is being used, it might be 443
)

# Optionally, we can have an Autoscaler to automatically scale the AI services based on the load they are receiving.
# This block can be repeated for multiple regional instance groups to ensure each region can scale independently.
autoscaler = gcp.compute.RegionAutoscaler('ai-service-region-autoscaler',
    target='INSTANCE_GROUP_MANAGER_URL',  # The URL of the target instance group manager
    autoscaling_policy=gcp.compute.AutoscalerAutoscalingPolicyArgs(
        min_replicas=1,  # Minimum number of instances in the instance group
        max_replicas=10,  # Maximum number of instances in the instance group
        cpu_utilization=gcp.compute.AutoscalerAutoscalingPolicyCpuUtilizationArgs(
            target=0.5  # Target CPU utilization for each instance
        ),
    ),
    region='us-central1',  # Region where the autoscaler will be deployed
)

# Export the URL of the Global Load Balancer to access the AI Services.
pulumi.export('ai_service_global_load_balancer_ip', global_forwarding_rule.ip_address)
```

In this example, we initialize a BackendService to manage our instances. We specify a backend using instance groups that contain our service, along with a health check to ensure that the load balancer only sends traffic to healthy instances.

A GlobalForwardingRule is created to expose our BackendService to the internet on port 80, the standard HTTP port. For HTTPS, port 443 would be used and additional SSL certificates would need to be managed.

An optional Autoscaler is defined for one of the instance groups, specifying the minimum and maximum number of instances and the target CPU utilization for scaling. This enables our AI service to handle variable loads efficiently.

Lastly, we export the IP address of the Global Load Balancer, which would be the entry point to our globally distributed AI services on GKE.

Remember to replace placeholder strings like 'INSTANCE_GROUP_URL', 'HEALTH_CHECK_URL', and 'INSTANCE_GROUP_MANAGER_URL' with actual resource URLs from your deployment. This program assumes the AI services and necessary health checks have already been set up in GKE.

This program should be part of a more comprehensive Pulumi project where the resources such as clusters, instance groups, and health checks have actual definitions or references. The autoscaling behavior should be monitored and tuned to ensure cost effectiveness and performance.