1. High Availability for AI-Based Applications Using DigitalOcean Load Balancer


    To set up high availability for AI-based applications using DigitalOcean, you need to ensure that your application is running on multiple Droplets (virtual machines) and that traffic is distributed among them. A DigitalOcean Load Balancer can automatically distribute incoming traffic across your infrastructure to enhance your application's availability and reliability.

    Here's how you can achieve high availability using Pulumi with DigitalOcean:

    1. Droplets: These are the compute instances that will run your application. You should configure them in a way that suits the requirements of your AI application, such as CPU, memory, and storage.

    2. Load Balancer: This service will distribute traffic across your Droplets evenly, ensuring that no single server becomes a bottleneck. It also checks the health of the Droplets and only sends traffic to the healthy ones.

    3. Floating IP: An IP address that can be instantly moved from one Droplet to another in the same datacenter, which is useful for redirecting traffic in case of failure.

    4. Domain and DNS: An approachable way for users to reach your application instead of using an IP address. You would configure a domain to point to your Load Balancer.

    In this Pulumi program, we'll create two Droplets and a Load Balancer to manage the traffic. Also, we'll assign a Floating IP to one of the Droplets.

    First, you'll need to install the required Pulumi package for DigitalOcean if you haven't already:

    pulumi plugin install resource digitalocean v4.22.0

    Now, let's write a Pulumi program in Python to create this setup:

    import pulumi import pulumi_digitalocean as digitalocean # Configuration for the Droplets droplet_size = "s-1vcpu-1gb" region = "nyc3" image = "ubuntu-20-04-x64" # Create two Droplets for high availability droplet_1 = digitalocean.Droplet("droplet-1", size=droplet_size, region=region, image=image ) droplet_2 = digitalocean.Droplet("droplet-2", size=droplet_size, region=region, image=image ) # Create a Load Balancer to distribute traffic between Droplets load_balancer = digitalocean.LoadBalancer("lb", region=region, forwarding_rules=[{ "entry_protocol": "http", "entry_port": 80, "target_protocol": "http", "target_port": 80, }], health_checks=[{ "protocol": "tcp", "port": 80, }], droplet_ids=[droplet_1.id, droplet_2.id] ) # Assign a Floating IP to the first Droplet for a stable IP address floating_ip = digitalocean.FloatingIp("floating-ip", region=region ) floating_ip_assignment = digitalocean.FloatingIpAssignment("ip-assignment", droplet_id=droplet_1.id, ip_address=floating_ip.ip_address ) # Exports - These URLs can be used to access your application pulumi.export("load_balancer_ip", load_balancer.ip) pulumi.export("floating_ip_address", floating_ip.ip_address)

    This program sets up a pair of Droplets with a Load Balancer in front to distribute traffic. It also assigns a Floating IP to one of the Droplets. Both the Load Balancer's IP and the Floating IP are exported at the end, which you can use to access your application.

    Make sure you've set up your Pulumi DigitalOcean token and are logged in to Pulumi by following the instructions on the Pulumi DigitalOcean provider setup page. Once you've done that, you can run pulumi up to create the infrastructure.

    This basic setup should get you started with high availability on DigitalOcean with Pulumi. Over time you might want to include additional configurations such as:

    • HTTPS termination at the Load Balancer to secure your application.
    • Domain name setup using DNS records pointing to the Load Balancer's IP.
    • More advanced health checks to ensure the AI application is working correctly.
    • Scaling out with more Droplets as needed.
    • Automating deployment so that code updates are smoothly rolled out to the Droplets.