Monolith-to-Microservice Redesign Using Static and Dynamic Analysis
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Solution Overview
Problem
Current processes for transforming monolithic software architectures into microservices are manual, time-consuming, costly, and error-prone, with challenges in data structuring, computational inefficiency, and increased complexity in inter-service messaging, while existing methods like IBM's overlook runtime data analysis and noise reduction.
Innovation Solution
A system and method utilizing dynamic and static analysis, AI techniques, and automated tools to extract discrete functional elements from monolithic systems, forming them into microservices, with automated validation and deployment, enabling a serverless infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes are used to transform monolithic architectures into microservices, then human expertise can guide decision making, but the process becomes time-consuming, costly and error prone
Solution Approach 1:
The system performs self-analysis of the monolithic codebase using automated static and dynamic analysis tools, extracting microservices without requiring continuous human intervention. The automated system guides its own transformation process while human experts provide initial input and validate results.
Solution Approach 2:
Manual mechanical processes of code analysis and microservice extraction are replaced with automated computational systems using AI and machine learning algorithms. These systems automatically analyze code dependencies, identify service boundaries, and generate microservice architectures.
2Ease of manufacture
If all data is stored in one large database in monolithic architecture, then data access is simple, but it becomes computationally inefficient and difficult to structure data on a per-service exclusivity basis
Solution Approach 1:
The monolithic database is segmented into multiple service-specific databases or data stores, with each microservice having its own dedicated data storage. This segmentation enables efficient data access for each service while maintaining overall system coherence through standardized access patterns.
Solution Approach 2:
Each microservice is assigned local data storage optimized for its specific functional requirements. This allows each service to have data structures and access patterns tailored to its needs, improving computational efficiency while maintaining simplicity through localized data management.
3Adaptability or versatility
If complex business functions are implemented in monolithic architecture, then all functionality is available in one system, but it becomes difficult to add new features without replacing operational business logic
Solution Approach 1:
The monolithic system is segmented into independent microservices, each encapsulating specific business functions. This segmentation enables new features to be added by creating or modifying individual services without affecting the entire system, thereby improving adaptability while managing complexity through modular organization.
Solution Approach 2:
The architecture transitions from a static monolithic structure to a dynamic microservices architecture where services can be independently created, modified, deployed, and scaled. This dynamic structure enables flexible adaptation to changing requirements while maintaining manageable complexity through service independence.
Data Source
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AI summary
The present invention provides a system and method for automation of the creation of a software application as a combination of microservices by extraction of discrete elements of software functionality from a monolithic architectures using dynamic and static analysis of code and non-code-related artefacts; forming them into microservices such that the software application is recreated as serverless hardware infrastructure while also validating the transformed code. In many cases, serverless microservices will significantly reduce the hardware requirement for software systems.