1. Time-Based Auto-Scaling for Batch Processing Workloads


    Auto-scaling for batch processing workloads is a technique used to dynamically adjust the number of compute resources dedicated to batch jobs based on the current workload and a predefined schedule. By using time-based auto-scaling, you cater the resource provisioning to predictable workloads that peak at certain times, ensuring that you have enough compute power when needed, while saving on costs during off-peak times.

    In Pulumi, you can manage time-based auto-scaling across various cloud providers such as AWS, Google Cloud, and Azure. Depending on the chosen provider, you would utilize different services tailored for batch processing, such as AWS Batch, Google Cloud Dataproc, or Azure Batch.

    Below is an example using AWS services to create an auto-scaling environment for batch processing. AWS Batch enables you to run batch computing workloads on the AWS Cloud, where you can define job queues, compute environments, and job definitions. The service handles the complexities of scheduling and scaling the underlying compute infrastructure for you.

    In this example, we'll set up an AWS Batch Compute Environment that scales the numbers of EC2 instances based on a time-based schedule. We'll use aws.batch.SchedulingPolicy and aws.batch.ComputeEnvironment to define the scheduling policy and the environment that should scale according to this policy.

    Here's a Python program using Pulumi to create such an environment:

    import pulumi import pulumi_aws as aws # Define the SchedulingPolicy for your batch jobs. # Scheduling policies in AWS Batch enable you to prioritize jobs and control the order in which they are run. scheduling_policy = aws.batch.SchedulingPolicy("exampleSchedulingPolicy", fair_share_policy={ "compute_reservation": 1, "share_decay_seconds": 3600, "share_distributions": [ { "share_identifier": "HighPriority", "weight_factor": 0.5 }, ], }) # Define a ComputeEnvironment for your batch workloads. # This environment will be where our batch jobs are executed. # We use an ON_DEMAND type to start with, but this could be adjusted to use SPOT instances as needed. compute_environment = aws.batch.ComputeEnvironment("exampleComputeEnvironment", service_role=aws_iam_role.example.arn, # Replace with an appropriate IAM role ARN compute_resources={ "type": "EC2", "min_vcpus": 0, # Start with zero capacity to minimize costs "max_vcpus": 100, # Maximum capacity for peak times "instance_types": ["m4.large"], # Instance types to use "subnets": [ # Replace with your VPC Subnet IDs "subnet-abcdefgh", "subnet-ijklmnop", ], "security_group_ids": [ # Replace with your Security Group IDs "sg-12345678", ], # Use an EC2 launch template if needed # "launch_template": { # "launch_template_name": "myLaunchTemplate", # "version": "$Latest" # }, "allocation_strategy": "BEST_FIT_PROGRESSIVE", }, type="MANAGED") # Output the ARN of the Scheduling Policy and Compute Environment. pulumi.export("scheduling_policy_arn", scheduling_policy.arn) pulumi.export("compute_environment_arn", compute_environment.arn)

    In this program:

    • We create a SchedulingPolicy with a fair share policy. Fair share policy ensures that jobs with different priority levels share compute capacity fairly over time.
    • We create a ComputeEnvironment with a range of min_vcpus and max_vcpus. This means that the compute environment will scale from min_vcpus instances when there are no jobs running, up to max_vcpus instances during peak times.
    • We specify the EC2 instance types suited for our workload with the instance_types parameter.
    • We select the BEST_FIT_PROGRESSIVE allocation strategy to optimize the placement of jobs based on available resources.

    Make sure to replace the placeholder values (such as IAM role ARN, subnet IDs, and security group IDs) with your actual values. The IAM role should have policies attached that allow for the necessary AWS Batch operations.

    This program doesn't define the actual schedule or auto-scaling actions based on time. To implement a time-based auto-scaling, you'd need additional configurations using AWS services like AWS Lambda and CloudWatch Events or using third-party schedulers.

    Please ensure that you have the AWS CLI configured with the necessary access rights and the Pulumi CLI installed and set up before running this program.