As part of executing a large-scale enterprise-wide cloud migration program, our client, a Tier 1 bank was looking for accelerators and techniques to prioritize legacy applications to be migrated to cloud and optimize the complex transformation process. The client successfully used our solution to shorten time-to-market and accelerate their systems modernization journey, particularly in the planning phase.
Using our methodology, we created a tool to facilitate legacy code analysis statically (without interrupting the live production environment), and then fed the output generated by this tool into machine learning algorithms to help define the shape and structure of an optimized collection of microservices.
The client was looking to evolve from a legacy system architecture to scalable architecture in an optimized way to ensure quality in the achieved results, with low costs and low complexity in future maintenance.
To take advantage of modern technologies, it was important to the client to develop this architectural evolution to microservices in an organized and standardized way, following the principles of governance, agility and automation and using AI-based learning.
It was equally essential to the client to make the right architectural decisions early in the design process, to ensure delivery with stability and maximum application performance, carefully selecting the granularity of the services layer to avoid errors that are difficult and expensive to fix.
Using our automated tool, the bank successfully refactored their COBOL monolithic applications into microservices candidates - for over 20 business services. This also created a reusable standardization approach for defining functional and technical taxonomies to be applied in future monolith refactoring initiatives and system migrations. Further impacts included:
