1. DDoS-Protected AI Model Serving with Kubernetes


    To create a DDoS-protected AI model serving infrastructure with Kubernetes, we will walk through the steps required to set up a Kubernetes cluster with the necessary components to serve the AI model securely. We'll containerize the AI model as a service, deploy it within the Kubernetes cluster, and then protect the cluster's ingress with a DDoS protection service.

    Let's go step by step:

    1. Set up the Kubernetes Cluster: We will create a Kubernetes cluster where our machine learning models will be served. Depending on the cloud provider, you could use AKS (Azure Kubernetes Service), EKS (Amazon Elastic Kubernetes Service), or GKE (Google Kubernetes Engine). For this example, I'll assume we're using Google Kubernetes Engine (GKE), which can be created using the pulumi_gcp package.

    2. Deploy the AI Model as a Service: Once the cluster is up and running, a Docker container image containing the AI model would be pushed to a registry (e.g., Google Container Registry). Kubernetes deployment and service objects manage the deployment and network access.

    3. Implement DDoS Protection: On top of the Kubernetes services, we can implement DDoS protection. Google Cloud offers Cloud Armor, which can be used in conjunction with Google Cloud Load Balancer to protect against DDoS attacks. When setting up ingress for our Kubernetes services, we would configure Cloud Armor security policies and attach them to the load balancer's backend services.

    Here's a basic Pulumi program in Python for serving a containerized AI model within a GKE cluster protected by Cloud Armor.

    import pulumi from pulumi_gcp import container from pulumi_gcp import compute # Step 1: Create a GCP Kubernetes Engine (GKE) cluster cluster = container.Cluster("ai-model-serving-cluster", initial_node_count=3, node_config=container.ClusterNodeConfigArgs( machine_type="n1-standard-1", oauth_scopes=[ "https://www.googleapis.com/auth/compute", "https://www.googleapis.com/auth/devstorage.read_only", "https://www.googleapis.com/auth/logging.write", "https://www.googleapis.com/auth/monitoring", ], )) # Step 2: Deploy the AI Model as a service in the cluster (the specific details of the model serving application will vary) # Normally, you would have a Docker image prepared and pushed to a registry like Google Container Registry (GCR) # For simplicity, this example assumes the Docker image is already available as `gcr.io/my-project-id/my-ai-model:v1` model_app_deployment = container.Deployment("ai-model-deployment", spec=container.DeploymentSpecArgs( selector=container.DeploymentSpecSelectorArgs( match_labels={"app": "ai-model"}, ), replicas=2, template=container.DeploymentSpecTemplateArgs( metadata=container.DeploymentSpecTemplateMetadataArgs( labels={"app": "ai-model"}, ), spec=container.DeploymentSpecTemplateSpecArgs( containers=[ container.DeploymentSpecTemplateSpecContainersArgs( name="ai-model-container", image="gcr.io/my-project-id/my-ai-model:v1", ports=[container.DeploymentSpecTemplateSpecContainersPortsArgs(container_port=80)], ), ], ), ), ), __opts__=pulumi.ResourceOptions(depends_on=[cluster])) # Step 3: Protect the AI model serving infrastructure using Google Cloud Armor # Create a Cloud Armor security policy security_policy = compute.SecurityPolicy("ai-model-security-policy", description="DDoS protection policy for AI model serving", rules=[ compute.SecurityPolicyRuleArgs( action="allow", priority=1000, match=compute.SecurityPolicyRuleMatchArgs( versioned_expr="SRC_IPS_V1", config=compute.SecurityPolicyRuleMatchConfigArgs( src_ip_ranges=["*"], ), ), ), ]) # Create a managed instance group for the GKE nodes # Note: In a real-world scenario, you would need to configure the instance template and autoscaling, among other settings instance_group_manager = compute.InstanceGroupManager("gke-nodes-instance-group", base_instance_name="gke-nodes", instance_template=cluster.instance_group_urls[0], target_size=3, named_port=compute.InstanceGroupManagerNamedPortArgs( name="http", port=80, )) # Configure the backend service with the Cloud Armor security policy backend_service = compute.BackendService("ai-model-backend-service", backends=[ compute.BackendServiceBackendArgs( group=instance_group_manager.instance_group, ), ], health_checks=[], security_policy=security_policy.self_link) # Attach backend service to a URL map and setup a forwarding rule (through a load balancer) url_map = compute.URLMap("ai-model-url-map", default_service=backend_service.self_link) forwarding_rule = compute.GlobalForwardingRule("ai-model-forwarding-rule", target=url_map.self_link) # Export the IP address of the load balancer to be accessed by clients pulumi.export("lb_ip_address", forwarding_rule.ip_address)

    This program outlines the primary resources we need in Pulumi to achieve a DDoS-protected AI model serving infrastructure using Kubernetes on Google Cloud:

    • container.Cluster: Represents our Kubernetes cluster on Google Cloud.
    • container.Deployment: Is how we deploy our AI model. The deployment will ensure that we have a specified number of replicas of our model serving container.
    • compute.SecurityPolicy: This is our Cloud Armor security policy, where we can specify rules to filter traffic.
    • compute.InstanceGroupManager: Handles our cluster nodes as an instance group that autoscales based on demand.
    • compute.BackendService: Sets up the backend service for our load balancer, using the nodes of our cluster. It ties in the security policy for DDoS protection.
    • compute.URLMap and compute.GlobalForwardingRule: Set up the external HTTP(S) load balancer, which will route traffic to our deployment.

    This is a simplified representation and does not include all the details, such as setting up the container registry, detailed networking settings, health checks for the backend service, and other production-grade considerations. Also, this example does not cover setting up the actual AI model serving code, which would typically be a separate step prior to deploying with Pulumi.