1. Autoscaling AI Model Servers with DigitalOcean Load Balancer


    To autoscale AI model servers on DigitalOcean, we will need to create a setup that consists of several components:

    1. Droplets: These are the virtual machines that will run your AI model servers.
    2. LoadBalancer: A DigitalOcean LoadBalancer will distribute incoming traffic across your Droplets to balance the load.
    3. Tags: These will be used to categorize Droplets for the LoadBalancer to know which Droplets to distribute traffic to.
    4. Health Checks: To ensure traffic is only sent to active Droplets, we set up health checks that the LoadBalancer will use to determine the health of the Droplets.
    5. Autoscaling: While DigitalOcean does not offer a native autoscaling service as of my last update, we can simulate autoscaling using Droplet tags and external monitoring and scaling mechanisms. For the scope of this Pulumi program, we'll create the initial setup.

    In our Pulumi program, we will:

    • Define a number of Droplets to serve as our AI model servers, considering that they all should be identical for scaling purposes.
    • Create a DigitalOcean LoadBalancer resource that will be configured to distribute traffic to Droplets tagged with a specific tag.
    • Configure health checks on the LoadBalancer to ensure traffic is routed only to healthy Droplets.

    Below is a detailed Pulumi Python program that sets up the described environment. This setup does not dynamically autoscale, but you can trigger scaling actions based on metrics from monitoring services (like DigitalOcean Monitoring or external ones like Prometheus) with some additional scripting or by using third-party services which could watch the metrics and use the DigitalOcean API to scale up or down by adding/removing Droplets.

    import pulumi import pulumi_digitalocean as digitalocean # Number of initial Droplets for the AI model servers initial_droplet_count = 3 # Defining the names for your Droplets and Load Balancer droplet_name_base = "ai-model-server" load_balancer_name = "ai-model-servers-lb" # Create a tag for all our AI model servers ai_model_servers_tag = digitalocean.Tag("ai-model-servers") # Create a number of Droplets tagged with 'ai-model-servers' droplets = [] for i in range(initial_droplet_count): droplet = digitalocean.Droplet(f"{droplet_name_base}-{i}", image="docker-18-04", region="nyc3", size="s-1vcpu-1gb", tags=[ai_model_servers_tag.name] ) droplets.append(droplet.id) # Configure health checks for the Load Balancer health_check = digitalocean.LoadBalancerHealthcheckArgs( port=80, protocol="tcp" ) # Setup the LoadBalancer to distribute traffic across Droplets with our tag load_balancer = digitalocean.LoadBalancer(load_balancer_name, name=load_balancer_name, region="nyc3", forwarding_rules=[digitalocean.LoadBalancerForwardingRuleArgs( entry_protocol="http", entry_port=80, target_protocol="http", target_port=80, )], healthcheck=health_check, droplet_tag=ai_model_servers_tag.name ) # Export the Load Balancer IP to access the service pulumi.export("load_balancer_ip", load_balancer.ip)

    Here’s what this program does:

    • It creates a tag called ai-model-servers.
    • It then spins up a specified number of Droplets with Docker installed, which you can use to deploy your AI model containers. These Droplets are tagged with ai-model-servers, allowing our LoadBalancer to identify them.
    • Next, it sets up a LoadBalancer with health checks on port 80 (TCP protocol). The health checks can be configured to meet the specifications of your AI models' health endpoints.
    • It creates forwarding rules to route traffic from port 80 on the LoadBalancer to port 80 on the tagged Droplets.
    • Finally, it exports the IP address of the Load Balancer so that you can distribute this IP to your clients or DNS services.

    Remember, this is a staging setup for autoscaling. To achieve fully-managed autoscaling, you will need to integrate additional monitoring and scaling logic. Auto-scaling typically requires observing load metrics (like average CPU usage or memory consumption) over a period of time, and then making API calls to increase or decrease the number of Droplets based on those metrics.