Mainframe migration and modernization with AI

For too long, enterprises with legacy mainframe estates have been faced with a high-stakes dilemma: continue maintaining their mainframes, essentially kicking the modernization can down the road (they know they will need to deal with it eventually), or perform a dangerous “big bang” migration with many unknowns and risks. 

At Google Cloud, we propose an alternative: a modernization strategy that leverages the power of AI, agility of the cloud and allows for iterative and continuous modernization. This approach recognizes a fundamental truth: mainframe modernization isn’t a pure code-to-code conversion problem. Sure, modernizing a single, isolated and small application is relatively easy, especially with recent advancements with AI and large language models. The real challenge lies in modernizing at real-world scale without breaking the intricate web of dependencies and legacy data formats you find in a large global enterprise, all while ensuring functional equivalence.

For example, some of these “real world” challenges include:

1. Application logic is tightly fused directly to legacy and proprietary databases and record schemas.

2. Non-relational formats that are inaccessible by AI Agents, such as VSAM, flat files, IMS hierarchical structures.

3. Transaction monitors like CICS and IMS TM deliver highly integrated transaction management. A single transaction scenario can consist of millions of lines of code. 

4. Intricate sequential workflows with complex conditional step logic and dependencies.

5. Internal/external boundaries utilizing proprietary protocols like CTG, IMS Connect, MQ, LU 6.2 Sockets.

6. Deep operational lock-in with specialized proprietary mainframe utility suites.

In other words, real-world modernization of mainframe applications is so much more than converting COBOL to Java. You also need to modernize the underlying data models, handle decades of obscured application dependencies and interfaces and modernize the underlying data stores. Most importantly, you need to validate and de-risk the modern code with actual production traffic before going live.

Our approach combines the advanced reasoning and scale of our Gemini models for code understanding, with mainframe-specific modernization products to address real-world complexity and challenges. Our solutions span four core pillars: assessment, modernization, de-risking, and data migration.