1. Identifying Underutilized Instances in AI Research Environments.

    Python

    In order to identify underutilized instances in AI research environments using Pulumi, you would typically focus on collecting and analyzing metrics that give you an insight into instance usage. For cloud providers like AWS, Azure, and GCP, Pulumi can manage the provisioning of resources and configurational setup for monitoring, but Pulumi itself doesn’t provide the monitoring solution.

    However, Pulumi can be used to set up monitoring solutions provided by these cloud services, and depending on the metrics collected (e.g., CPU utilization, memory usage, disk I/O, network throughput), you can identify underutilized resources.

    For example, with AWS, you could use CloudWatch to monitor EC2 instances, setting up alarms for low utilization thresholds which can then prompt further manual or automated inspection or action, such as instance resizing or stopping to save costs. Here’s a Pulumi program that sets up basic monitoring for an existing EC2 instance:

    import pulumi import pulumi_aws as aws # Assume that we already have an instance created, and we want to monitor its CPU utilization. # Here's how we would set up a CloudWatch alarm for low CPU utilization. # The `instance_id` variable would typically come from the EC2 instance you've provisioned. # E.g., `instance = aws.ec2.Instance('my-instance', ...)` # Then you would set `instance_id = instance.id` instance_id = "i-1234567890abcdef0" # Example instance ID # Create CloudWatch Metric Alarm for CPU utilization cpu_utilization_alarm = aws.cloudwatch.MetricAlarm("lowCpuUtilization", comparison_operator="LessThanOrEqualToThreshold", evaluation_periods=1, metric_name="CPUUtilization", namespace="AWS/EC2", period=300, statistic="Average", threshold=10.0, alarm_description="This metric monitors ec2 cpu utilization", dimensions={"InstanceId": instance_id}, alarm_actions=["arn:aws:sns:us-west-2:444455556666:my-sns-topic"], # Replace with your SNS topic ARN insufficient_data_actions=["arn:aws:sns:us-west-2:444455556666:my-sns-topic"], # Replace with your SNS topic ARN ) pulumi.export("cpuUtilizationAlarmName", cpu_utilization_alarm.name)

    In the above program, we're creating an aws.cloudwatch.MetricAlarm resource named lowCpuUtilization which will trigger when the CPU utilization of the specified EC2 instance (instance_id) is less than or equal to 10%.

    • comparison_operator: Defining the condition to consider when evaluating the metric data relative to the threshold.
    • evaluation_periods: The number of periods over which data is compared to the specified threshold.
    • metric_name: The name for the alarm's associated metric.
    • namespace: The namespace for the alarm's associated metric.
    • period: The period in seconds over which the statistic is applied.
    • statistic: The statistic for the metric associated with the alarm.
    • threshold: The value against which the specified statistic is compared.

    The alarm_description gives a human-readable explanation of what the alarm monitors, and the dimensions map the alarm to the specific instance.

    The alarm_actions and insufficient_data_actions have been set to notify an SNS topic when the alarm state changes; you'd replace the indicated ARN with the one for your SNS topic.

    This is a simplified example to set up a basic alarm. A full solution for identifying underutilized instances would also track additional metrics, possibly include scaling policies based on those metrics, and might integrate with other services for automated response actions.

    Please note that this monitoring setup requires that you have an AWS account set up with the proper permissions and that you've configured your Pulumi AWS provider accordingly.


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