Smartcollegeai: BMC Control-M to automate the collection and analysis of data center performance metrics, facilitating resource optimization and efficiency improvements

Customer Information & Request
Name: Rohan Duhaney
Your magic request: "Write a code snippet to solve Performance Optimization Automation: "Employ BMC Control-M to automate the collection and analysis of data center performance metrics, facilitating resource optimization and efficiency improvements." Please use fictition data for a large scale company using Python."
Email: smartduhaney@gmail.com

Dear Rohan Duhaney,

# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import controlm_api # Import BMC Control-M API library

# Set up connection to BMC Control-M API
controlm = controlm_api.ControlMAPI()
controlm.connect()

# Define function to retrieve performance metrics for a specific job or workflow
def get_performance_metrics(job_name):
    # Retrieve job status and duration data from BMC Control-M
    job_status = controlm.get_job_status(job_name)
    job_duration = controlm.get_job_duration(job_name)

    # Calculate average duration and success rate over the past month
    avg_duration = np.mean(job_duration)
    success_rate = np.sum(job_status == 'SUCCESS') / len(job_status)

    # Return a dictionary with the performance metrics
    return {'Job Name': job_name,
            'Average Duration': avg_duration,
            'Success Rate': success_rate}

# Define function to optimize resources based on performance metrics
def optimize_resources(metrics_df):
    # Calculate the cost of each job based on its duration and success rate
    metrics_df['Cost'] = metrics_df['Average Duration'] * (1 - metrics_df['Success Rate'])

    # Sort jobs by cost in descending order
    sorted_metrics = metrics_df.sort_values(by='Cost', ascending=False)

    # Set a threshold for the total cost of all jobs
    total_cost_threshold = 1000000

    # Loop through the jobs and optimize resources
    for index, row in sorted_metrics.iterrows():
        # Check if the total cost is above the threshold
        if sorted_metrics['Cost'].sum() > total_cost_threshold:
            # If yes, reduce the resource allocation for the job with the highest cost
            controlm.set_resource_allocation(row['Job Name'], 0.8)

    # Print a message to confirm the resource optimization
    print("Resource optimization completed successfully.")

# Retrieve performance metrics for all jobs and workflows in the data center
all_jobs = controlm.get_all_jobs()
all_workflows = controlm.get_all_workflows()

# Create data frame with performance metrics for all jobs and workflows
performance_metrics = pd.DataFrame(get_performance_metrics(job) for job in all_jobs + all_workflows)

# Optimize resources based on performance metrics
optimize_resources(performance_metrics)

# Disconnect from BMC Control-M API
controlm.disconnect()
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