Light Weight Mainframe Orchestration Engine

A method for automating integration, validation, and deployment of mainframe applications within a Continuous Integration/Continuous Deployment (CI/CD) pipeline using a system comprising BEARDS (Build Engineering And Rapid Development System), the method comprising:

• continuously monitoring, by a CI/CD process, a source control system to detect changes in the source code of a mainframe application, including tracking modifications to repository structure, branch updates, individual code commits, and merge operations, where the monitoring involves and automated triggers for subsequent build processes;
• retrieving, by BEARDS, modified source code, including associated metadata, version information, and dependencies, from the source control system, ensuring that all relevant branches, commit histories, and ongoing merges are incorporated into the retrieval process to maintain the integrity of a codebase;
• dynamically querying, by BEARDS, a dependency database to identify and resolve additional dependencies required for the build process, where the querying includes automatically updating the dependency database with any new dependencies detected, after the completion of the build, resolving conflicts, and ensuring that all dependencies are correctly configured for a mainframe environment;
• generating, by BEARDS, Job Control Language (JCL) jobs customized to specific requirements of the mainframe environment, where the generation process involves dynamically adjusting compiler options, memory allocations, and execution parameters based on a real-time analysis of the mainframe's resource availability, current system load, and configuration settings;
• initiating, by BEARDS, a build process within the mainframe environment using the generated JCL jobs to compile the source code into executable artifacts, where the build process includes preprocessing steps for files not natively supported by the mainframe to ensure compatibility, as well as integrating additional build steps tailored to optimize performance, resource usage, and error handling;
• performing, by BEARDS, a rigorous validation of downloaded artifacts using a SHA-256 hash algorithm, where hash values are generated both before and after the transfer process from the mainframe into a directory for the CI/CD process to ensure the integrity of the artifacts and verify that they have not been altered during the transfer;
• generating, by BEARDS, detailed YML files containing comprehensive deployment instructions specific to the mainframe environment, where the YML files include configurations, dataset assignments, pre-deployment validation checks, environment-specific parameters, and rollback procedures that are necessary to ensure a successful and optimized deployment process;
• securely transferring, by the CI/CD process, the validated compiled artifacts to an artifact repository that is version-controlled, such as JFrog Artifactory, where the transfer process includes generating a new SHA-256 hash post-transfer to ensure the integrity of the artifacts during storage, managing multiple versions of the artifacts, and associating each version with its respective hash value for easy retrieval, validation, and deployment;
• executing, by XLRelease and Ansible, the deployment of the validated and versioned compiled artifacts from the artifact repository to the mainframe environment, utilizing the generated YML files to ensure that the deployment is carried out consistently, efficiently, and securely across multiple environments, including development (DEV), quality assurance (QA), and production (PROD);
• managing, by XLRelease and Ansible, the post-deployment process, including automatically generating and distributing detailed deployment reports to stakeholders, which include the results of the SHA-256 hash validation, performance metrics, and any issues encountered during deployment, as well as updating deployment status logs, providing real-time feedback on deployment progress, and archiving the reports for future reference and compliance purposes;
• securing, by XLRelease and Ansible, the entire deployment process through encrypted communication channels, role-based access controls, and immutable audit trails, where all deployment actions are logged, digitally signed, timestamped, and stored in a tamper-proof environment to ensure compliance with security policies and regulatory requirements;
• scaling, by XLRlease and Ansible, the deployment process across multiple environments by optimizing task parallelization, managing resource allocation to reduce bottlenecks, and retaining and caching large artifacts within the mainframe environment to minimize the need for repeated external transfers, thereby enhancing deployment speed, reducing latency, and ensuring consistent performance, particularly in high-volume, enterprise-level operations;
• enabling, by BEARDS, incremental builds where only modified or updated source code files are recompiled and redeployed, reducing the overall time, resource usage, and risk associated with each build, while maintaining the integrity and consistency of the deployed application across all target environments, and ensuring that subsequent builds integrate seamlessly with previous deployments;
• and
• integrating, by BEARDS, with cloud-based services to support hybrid cloud environments, where artifacts and configurations are seamlessly transferred and deployed between on-premise mainframe systems and cloud-based infrastructure, providing flexibility, scalability, and the ability to leverage cloud resources for testing, deployment, and scaling operations, while maintaining the security and integrity of the mainframe applications.

A system for automating integration, validation, and deployment of mainframe applications within a Continuous Integration/Continuous Deployment (CI/CD) pipeline, the system comprising:

• a source control module configured to continuously monitor a source control system for changes in the source code of a mainframe application, wherein the module is further configured to detect modifications to repository structure, branch updates, individual code commits, and merge operations, and to trigger subsequent build processes in response to detected changes;
• a retrieval module configured to retrieve modified source code, including associated metadata, version information, and dependencies, from the source control system, wherein the module ensures that all relevant branches, commit histories, and merge operations are incorporated into the retrieval process;
• a dependency management module configured to dynamically query a dependency database to identify and resolve additional dependencies required for the build process, wherein the module is further configured to automatically update the dependency database with any new dependencies detected during the build, resolve conflicts, and ensure that all dependencies are correctly configured for a mainframe environment;
• a JCL generation module configured to generate Job Control Language (JCL) jobs customized to specific requirements of the mainframe environment, wherein the module dynamically adjusts compiler options, memory allocations, and execution parameters based on a real-time analysis of the mainframe's resource availability, system load, and configuration settings;
• a build module configured to initiate a build process within the mainframe environment using the generated JCL jobs to compile the source code into executable artifacts, wherein the module further includes preprocessing capabilities for files not natively supported by the mainframe, and is configured to integrate additional build steps tailored to optimize performance, resource usage, and error handling;
• a validation module configured to perform rigorous validation of the transferred artifacts, from the mainframe into a directory for a CI/CD process, using a SHA-256 hash algorithm, wherein the module generates hash values before and after the transfer process to ensure the integrity of the artifacts, and verify that they have not been altered during the transfer;
• a YML generation module configured to generate detailed YML files containing comprehensive deployment instructions specific to the mainframe environment, wherein the YML files include configurations, dataset assignments, pre-deployment validation checks, environment-specific parameters, and rollback procedures to ensure a successful and optimized deployment process;
• an artifact management module configured to securely transfer the validated compiled artifacts to an artifact repository that is version-controlled, such as JFrog Artifactory, wherein the module generates a new SHA-256 hash post-transfer to ensure the integrity of the artifacts during storage, manages multiple versions of the artifacts, and associates each version with its respective hash value for easy retrieval, validation, and deployment;
• a deployment module configured to execute the deployment of the validated and versioned compiled artifacts from the artifact repository to the mainframe environment, wherein the module utilizes the generated YML files and automation tools such as Ansible to ensure that the deployment is carried out consistently, efficiently, and securely across multiple environments, including development (DEV), quality assurance (QA), and production (PROD);
• a deployment validation module configured to validate the deployment process through automated functional and non-functional tests, including unit testing, integration testing, performance testing, and security testing, as well as SHA-256 hash-based validation to confirm that deployed artifacts match the versioned artifacts stored in the repository, and to update deployment status logs with detailed test results and deployment outcomes;
• a post-deployment management module configured to manage the post-deployment process, including automatically generating and distributing detailed deployment reports to stakeholders, which include the results of the SHA-256 hash validation, test outcomes, performance metrics, and any issues encountered during deployment, as well as updating deployment status logs, providing real-time feedback on deployment progress, and archiving the reports for future reference and compliance purposes;
• a security module configured to secure the deployment process through implementation encrypted communication channels, role-based access controls, and immutable audit trails, wherein all deployment actions are logged, digitally signed, timestamped, and stored in a tamper-proof environment to ensure compliance with security policies and regulatory requirements;
• a scaling module configured to scale the deployment process across multiple environments by optimizing task parallelization, managing resource allocation to reduce bottlenecks, and retaining and caching large artifacts within the mainframe environment to minimize the need for repeated external transfers, thereby enhancing deployment speed, reducing latency, and ensuring consistent performance, particularly in high-volume, enterprise-level operations;
• an incremental build module configured to enable incremental builds where only modified or updated source code files are recompiled and redeployed, reducing the overall time, resource usage, and risk associated with each build, while maintaining the integrity and consistency of the deployed application across all target environments, and ensuring that subsequent builds integrate seamlessly with previous deployments;
• and
• a cloud integration module configured to integrate with cloud-based services to support hybrid cloud environments, wherein the module enables seamless transfer and deployment of artifacts and configurations between on-premise mainframe systems and cloud-based infrastructure, providing flexibility, scalability, and the ability to leverage cloud resources for testing, deployment, and scaling operations, while maintaining the security and integrity of the mainframe applications.