Event-Driven Scaling for AI Processing Workloads

Question

Pulumi · Accepted Answer

When deploying AI processing workloads on a cloud infrastructure, it's common to use event-driven scaling to automatically adjust the number of compute resources in response to workload changes. This optimizes cost and ensures that the system can handle the workload without manual intervention.

To implement event-driven scaling, you can use various services depending on your cloud provider. For AWS, you'll typically incorporate services like AWS Lambda (for handling the events), Amazon SQS or SNS for messaging, and AWS Auto Scaling to adjust the compute capacity.

In this Pulumi program written in Python, I'll show you how to set up event-driven scaling for AI processing workloads on AWS. We'll use the following AWS services:

- AWS Lambda: To execute code in response to events.
- Amazon SQS (Simple Queue Service): To act as a messaging system, holding messages in a queue until processing can occur.
- AWS Auto Scaling with Auto Scaling Policies: To automatically scale Amazon EC2 instances up or down according to conditions we define.

Here is a step-by-step guide followed by the Pulumi program:

1. **Lambda Function**: We create an AWS Lambda function, which will be triggered by an event. For instance, it could process images uploaded to an S3 bucket, and the occurrence of such an event would trigger the scaling action.
   
2. **SQS Queue**: We set up an Amazon SQS queue where messages will be sent. These messages can be the workload for the AI processing tasks.

3. **Auto Scaling Policies**: We attach auto scaling policies to an EC2 Auto Scaling group. These policies define when to scale out (add more EC2 instances) or scale in (remove EC2 instances) based on the number of messages in the SQS queue.

```python
import pulumi
import pulumi_aws as aws

# Create an AWS Lambda function that will process your AI workload.
lambda_role = aws.iam.Role("lambdaRole",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Effect": "Allow",
            "Principal": {
                "Service": "lambda.amazonaws.com"
            }
        }]
    }""")

lambda_function = aws.lambda_.Function("aiProcessor",
    role=lambda_role.arn,
    handler="index.handler",
    runtime="python3.8",
    code=pulumi.FileArchive("./ai_processor.zip")) # This zip file should contain your Lambda function code.

# Create an SQS queue that will hold the events triggering the Lambda.
sqs_queue = aws.sqs.Queue("aiWorkloadQueue")

# Assume we have an existing AutoScaling Group for EC2 Instances ready.
# Replace 'your_auto_scaling_group_name' with your actual AutoScaling Group name.
auto_scaling_group = aws.autoscaling.Group.get("existingAutoScalingGroup", "your_auto_scaling_group_name")

# Auto Scaling Policy for scaling out.
scale_out_policy = aws.autoscaling.Policy("scaleOutPolicy",
    autoscaling_group_name=auto_scaling_group.name,
    adjustment_type="ChangeInCapacity",
    scaling_adjustment=2, # Number of instances to add during scaling out.
    cooldown=300, # The amount of time, in seconds, after a scaling activity completes before another can begin.
    policy_type="SimpleScaling")

# Auto Scaling Policy for scaling in.
scale_in_policy = aws.autoscaling.Policy("scaleInPolicy",
    autoscaling_group_name=auto_scaling_group.name,
    adjustment_type="ChangeInCapacity",
    scaling_adjustment=-1, # Number of instances to remove during scaling in.
    cooldown=300,
    policy_type="SimpleScaling")

# CloudWatch Alarm that triggers the scaling out policy based on the number of messages visible in the SQS queue.
cloudwatch_out_alarm = aws.cloudwatch.MetricAlarm("scaleOutAlarm",
    metric_name="ApproximateNumberOfMessagesVisible",
    namespace="AWS/SQS",
    statistic="Sum",
    period=60,
    evaluation_periods=2,
    threshold=10, # Trigger scaling out when there are more than 10 messages waiting.
    comparison_operator="GreaterThanOrEqualToThreshold",
    dimensions={
        "QueueName": sqs_queue.name,
    },
    alarm_actions=[scale_out_policy.arn])

# CloudWatch Alarm that triggers the scaling in policy based on the number of messages visible in the SQS queue.
cloudwatch_in_alarm = aws.cloudwatch.MetricAlarm("scaleInAlarm",
    metric_name="ApproximateNumberOfMessagesVisible",
    namespace="AWS/SQS",
    statistic="Sum",
    period=60,
    evaluation_periods=2,
    threshold=2, # Trigger scaling in when there are 2 or fewer messages waiting.
    comparison_operator="LessThanOrEqualToThreshold",
    dimensions={
        "QueueName": sqs_queue.name,
    },
    alarm_actions=[scale_in_policy.arn])

# Output the names of the resources we created.
pulumi.export('lambda_function_name', lambda_function.name)
pulumi.export('sqs_queue_name', sqs_queue.name)
```

In the code above:

- We define a Lambda function `aiProcessor` which contains the logic for processing your AI workload.
- We create an SQS queue `aiWorkloadQueue`, which is where your application will send messages to trigger the Lambda function.
- We have two auto scaling policies, `scaleOutPolicy` and `scaleInPolicy`, which increment or decrement the count of EC2 instances respectively.
- We create two CloudWatch alarms, `scaleOutAlarm` and `scaleInAlarm`, which watch the `ApproximateNumberOfMessagesVisible` metric on the SQS queue. When the metric crosses the defined threshold, the alarms trigger the scaling policies accordingly.

This approach enables your AI workload to scale based on the number of jobs that need to be processed, providing an efficient, cost-effective solution for event-driven scaling.