AWS Partner Network (APN) Blog

State Farm Increases Efficiency and Optimization by Integrating Control-M with AWS Mainframe Modernization Service

By Drew Jaegle, Staff Technology Engineer – State Farm
By Joe Goldberg, Innovation Evangelist – BMC
By Sunil Bemarkar, Vij Balakrishna, and Mansoor Khan – AWS

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To meet the needs of a digitally transformed era, businesses are constantly looking to modernize their legacy mainframe systems and infrastructure to keep up with the evolving technology landscape, particularly with public cloud.

Modernization helps organizations to increase their agility and capacity to innovate while benefitting from improved cost performance ratios. It also helps businesses gain access to a growing pool of candidates with experience running and automating workloads in the cloud.

State Farm, a leading insurance company, is one such company leading the way in modernizing its mainframe application. In 2021, State Farm embarked on the modernization of its core Property & Casualty Claims system.

This will be a multi-year journey that involves migrating multiple applications and datastores to Amazon Web Services (AWS). A portion of those supporting applications reside on the mainframe and are being incrementally migrated using the AWS Mainframe Modernization service for Blu Age.

In this post, we will demonstrate the application of new enhancements to the existing integration available between the Control-M workflow orchestrator and AWS Mainframe Modernization service. These enhancements are made available in the Git repository as an updated version of job type “AIJOB_v2.ctmai.”

BMC Software is an AWS Migration and Modernization Competency Partner that offers a full suite of solutions to help you migrate to and maximize your investment in AWS.


For over 100 years, the mission of State Farm has been and continues to be helping people manage the risks of everyday life, recover from the unexpected, and realize their dreams. State Farm and its affiliates are the largest providers of auto and home insurance in the United States.

Unprecedented levels of growth in business brings new challenges. State Farm had a sizable portion of its critical claims applications running on legacy mainframe systems. While these systems had served the organization well for decades, they posed several challenges in terms of agility, technical debt, and dependency on Cobol developers. As the State Farm IT workforce evolves, the availability of skilled mainframe developers shrinks and the need to mitigate the technology skill gap grows.

To modernize a large and complex mainframe batch job architecture, the need for a reliable job scheduling solution becomes important to the success of that migration. To avoid a big bang approach, the applications are not migrated at once. Having a hybrid job scheduler/manager able to control batch running on premises and on cloud is critical.

State Farm uses Control-M from BMC to manage 1,000 batch jobs daily. To preserve that investment, State Farm focused on how to extend and integrate Control-M to manage job streams across both mainframe and AWS Mainframe Modernization applications.

AWS Mainframe Modernization service helps customers to modernize their mainframe workloads with a managed and highly available runtime environment. It delivers features and value across the entire mainframe application migration, modernization, execution, and operation lifecycle. The service enables re-platforming, automated refactoring, and augmentation patterns. It provides tools and resources to help customers plan and implement migration and modernization.

This post will focus on the integration between AWS Mainframe Modernization service and Control-M to provide batch job scheduling and management. For more information on the broader modernization journey, see this State Farm Engineering article.


State Farm needed a solution that would modernize and improve its claims business with little disruption to the tribal knowledge their people developed over decades while working on legacy mainframe systems, specifically with mainframe terminology and the integrated batch scheduler which orchestrated their workflows to keep the business running.

State Farm decided to leverage Control-M for its deep operational capabilities with the AWS Mainframe Modernization service to preserve the continuity of mission-critical business outcomes. This was delivered by automating application and data workflows in production, across distributed, hybrid, and mainframe environments.

Control-M easily integrates, automates, and orchestrates applications and technologies with interfaces for IT operations, data engineers, developers, and business users across their on-premises and AWS platform.

The following architecture diagram depicts the final implementation of workflow integration where Control-M is hosted in an on-premises data center and is orchestrating applications’ batch jobs and data on the AWS Mainframe Modernization service.


Figure 1 – Architecture diagram.

As a solution, AWS Prescriptive Guidance became the starting point for implementing the Control-M workflow orchestrator integration with the AWS Mainframe Modernization approach. This guidance describes step-by-step setup required for integration between Control-M Scheduler and the AWS Mainframe Modernization environment, depicting different types of integration workflows.

The solution helps achieve the following objectives:

  • Extend Control-M environment to manage workloads on the AWS Mainframe Modernization service.
  • Incorporate Control-M REST APIs into processes quickly by using wizards.
  • Orchestrate, schedule, and monitor all your application workload jobs in the Control-M unified view.
  • Increase scalability and business agility.

This guidance provided a reference implementation of Control-M Application Integration component which was enhanced to better meet the needs of State Farm.

The following sequence diagram depicts Control-M workflow orchestration interacting with AWS Mainframe Modernization service and Amazon CloudWatch API as part of the workflow to orchestrate batch jobs.


Figure 2 – Sequence diagram.

The first enhancement was in the Control-M Application Integrator component to replace the “application id” property on the job configuration with an “application name”. This “application name” is then used in a pre-execution step calling the Mainframe Modernization ListApplications API to discover and check to see if the application was running before starting the job.

This enhancement allowed the application integration component to be more resilient to application re-deployments.


Figure 3 – Control-M Application Integrator job properties.    

The second enhancement was to improve the error handling of the Control-M Application Integrator component to fail the job if the AWS Mainframe Modernization application was not found or not running. This was achieved by adding pre-execution step in Control-M application integrator job to query the state of the AWS Mainframe Modernization application.

An additional execution step was included in the Control-M application integrator logging various job information in the Control-M job output log.

The third enhancement was to remove the “host” property on the Control-M Connection profile as it was not needed. The endpoints for AWS Mainframe Modernization and CloudWatch APIs only vary based on the AWS region being called and the connection profile provides that value.


Figure 4 – Control-M connection profile.

The fourth enhancement was to update the configuration for the logger used by Control-M, Logback, to include the job name on all log messages written to CloudWatch. This allowed for an enhancement to the retrieve CloudWatch logs step ensuring only the logs for that specific job execution are retrieved and written to the job output log.

AWS Mainframe Modernization App Logback config:

<?xml version="1.0" encoding="UTF-8"?>

	<!-- Hide logback configuration logging. -->
	<appender name="CONSOLE" class="ch.qos.logback.core.ConsoleAppender">
			<Pattern>%date{yyyy-MM-dd' 'HH:mm:ss.SSS} %-4r [%thread] [%X{jobName}] %-5level - %msg%n</Pattern>

	<root level="INFO">
		<appender-ref ref="CONSOLE" />


Cloud Watch Query Filter:

The enhancements mentioned above are provided as an updated version of job type AIJOB_v2.ctmai and supplied in the Git repository. To deploy this, you must clone the aws-mainframe-modernization-controlm-integration GitHub repository and download the AIJOB_v2.ctmai file onto a file system location that can be accessed by Application Integrator and imported as a job type.

You can deploy the job type by following the instructions in the Control-M documentation.

All of these enhancements made the solution more efficient by removing the constraint of an application_id, the value of which was directly tied to the mainframe modernization application deployments.

The Control-M workflow was optimized by proactively failing the job if mainframe modernization application is not in a running state. These enhancements helped State Farm minimize the long-term maintenance of Control-M integration with AWS Mainframe Modernization service.


Successful modernization of State Farm’s claims business using AWS Mainframe Modernization service and Control-M from BMC serves as a compelling case study for organizations looking to transform their legacy systems.

By embracing the cloud and adopting robust workload automation tools, State Farm achieved greater agility and operational efficiency while delivering an enhanced customer experience. This modernization journey highlights the transformative power of cloud computing and intelligent workload automation in today’s competitive business landscape.

Learn more about how State Farm is engineering and innovating to build a more accessible, customer-focused, and future for insurance, and how customers unlock core business processes, gain flexibility, and innovate with Mainframe Modernization on AWS. State Farm and the State Farm logo are registered trademarks of State Farm Mutual Automobile Insurance Company.


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