Monolithic Application Decomposition for Runtime-Aware Distributed Deployment

The system performs self-service by automatically analyzing the monolithic application's code structure, data dependencies, and execution patterns to generate decomposition proposals without requiring manual intervention. The automated analysis engine examines bytecode, identifies module boundaries based on cohesion metrics, and suggests decompositions that maintain data consistency, thereby reducing both cost and complexity while achieving deployment flexibility.

The patent replaces manual mechanical decomposition processes with automated computational analysis. Instead of manually reviewing code and making decomposition decisions, the system uses bytecode analysis, static analysis tools, and machine learning algorithms to automatically identify decomposition opportunities, evaluate data dependencies, and generate deployment configurations, significantly reducing the complexity and cost of the decomposition process.

The system applies local quality by analyzing data dependency patterns and identifying which modules have strong local couplings versus weak couplings. Modules with strong data dependencies are kept together or placed on the same host, while modules with weak dependencies can be independently deployed. This localized optimization reduces unnecessary network traffic between tightly-coupled modules while maintaining deployment flexibility for loosely-coupled modules.

The patent implements skipping by identifying and inlining small, frequently-called functions or methods that cross module boundaries. Instead of making separate network calls for these small operations, the system inlines the code locally, eliminating unnecessary network traffic for routine operations while maintaining the modular architecture for larger, more significant decomposition units.

The system performs preliminary static analysis to quickly identify potential decomposition points and data dependency patterns in the monolithic application's code structure. This fast initial analysis provides a baseline decomposition proposal that captures the majority of decomposition opportunities. Subsequently, dynamic analysis is applied to refine these proposals by observing actual runtime behavior, ensuring both speed and accuracy in the final decomposition.

The patent implements a continuous analysis approach where static analysis is performed first to establish a baseline decomposition, followed by dynamic analysis during runtime to capture actual behavior patterns. The system continuously refines the decomposition proposal by combining insights from both static and dynamic analyses, maintaining an ongoing optimization process that improves accuracy while managing analysis time through iterative refinement rather than exhaustive analysis.