Fault-Tolerant Model Serving with OpenStack

Question

Pulumi · Accepted Answer

To create a fault-tolerant model serving architecture with OpenStack, you will need to set up an environment that can handle the distribution of compute tasks and can recover from any partial failures without affecting the overall availability and functionality of the model serving application.

In the context of OpenStack, the following concepts play a vital role:

- **Compute Instances**: Virtual machines that serve your model; you can create multiple instances to handle different requests or serve different models.
- **Load Balancer**: Distributes incoming requests across your instances, ensuring even load and redundancy.
- **Auto Scaling**: Automatically scales your instances up or down based on load, ensuring that the model serving capacity matches the demand without manual intervention.
- **Instance Anti-Affinity**: Ensures that instances are spread across different physical hosts for high availability.

In the provided Pulumi Registry Results, we do not have a direct mapping for OpenStack resources since the focus is on AWS, Azure, vSphere, and others. However, creating a fault-tolerant model serving environment typically involves these steps regardless of the cloud provider:

1. Provisioning the compute resources.
2. Configuring auto-scaling to handle variable load.
3. Setting up a load balancer to distribute traffic.
4. Implementing health checks and redundancy.

Below, we will outline a general approach to achieving fault tolerance using Pulumi with cloud providers that closely resemble an OpenStack environment. Since Pulumi does not have a direct OpenStack provider, the following program will be hypothetical and intended to guide you through the concepts using AWS as an example. If you are using OpenStack, you should use an OpenStack-specific tool or SDK that facilitates infrastructure provisioning.

```python
import pulumi
import pulumi_aws as aws

# Example configuration to set up a fault-tolerant model serving architecture in AWS.

# Create an EC2 instance that will serve as the model server.
# Multiple instances will be created later with an Auto Scaling Group.
model_server_instance = aws.ec2.Instance("modelServerInstance",
    instance_type="t3.micro",
    ami="ami-123456" # Replace with a valid AMI for your region
)

# Create a Load Balancer to distribute the incoming traffic across multiple instances.
load_balancer = aws.lb.LoadBalancer("modelServingLB",
    internal=False,
    load_balancer_type="application",
    security_groups=["sg-123456"], # Replace with your security group
    subnets=["subnet-123456"] # Replace with your subnet
)

# Create a Target Group for the Load Balancer to know where to send the traffic.
target_group = aws.lb.TargetGroup("modelServingGroup",
    port=80,
    protocol="HTTP",
    vpc_id="vpc-123456" # Replace with your VPC ID
)

# Create a listener for the Load Balancer to check for incoming HTTP requests.
listener = aws.lb.Listener("modelServingListener",
    load_balancer_arn=load_balancer.arn,
    port=80,
    protocol="HTTP",
    default_actions=[{
        "type": "forward",
        "target_group_arn": target_group.arn
    }]
)

# Settings for the Auto Scaling Group.
scaling_policy = aws.autoscaling.Policy("modelServingScalingPolicy",
    policy_type="TargetTrackingScaling",
    autoscaling_group_name=model_server_group.name,
    target_tracking_configuration={
        "predefined_metric_specification": {
            "predefined_metric_type": "ASGAverageCPUUtilization"
        },
        "target_value": 50.0
    }
)

# Ensure that the Auto Scaling Group creates instances across multiple availability zones for fault-tolerance.
model_server_group = aws.autoscaling.Group("modelServerGroup",
    vpc_zone_identifiers=["subnet-123456", "subnet-654321"], # Replace with your subnets in different AZs
    desired_capacity=2,
    max_size=10,
    min_size=1,
    health_check_grace_period=300,
    health_check_type="ELB",
    force_delete=True,
    launch_configuration=model_server_instance.id,
    target_group_arns=[target_group.arn]
)

pulumi.export("load_balancer_dns", load_balancer.dns_name)
```

In the example above, we provision an EC2 instance, set up a Load Balancer with a target group and listener, and finally create an Auto Scaling Group to manage our instances. The Auto Scaling Group ensures that if an instance fails, another one is created automatically to replace it, maintaining the fault tolerance of the system. If the load increases, the group will add more instances to maintain performance, and if it decreases, the number of instances will decrease to reduce costs.

Keep in mind that translating this example to work with OpenStack would involve finding the OpenStack equivalents of the AWS resources (e.g., Nova for compute instances, Neutron for load balancers, Heat for auto-scaling), and using an appropriate tool or SDK that supports OpenStack. Pulumi currently does not have support for OpenStack, so you would not be able to use Pulumi for this purpose.