Web Service Entity Clustering for Microservice Refactoring
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Solution Overview
Problem
Identifying suitable groups of Web service entities in monolithic applications for refactoring into microservices is challenging due to the complexity of navigation relationships among hundreds to thousands of entities.
Innovation Solution
A system is developed to efficiently determine groups of Web service entities by generating an undirected graph based on navigation relationships, applying clustering algorithms such as spectral clustering, and presenting the grouped entities for refactoring into microservices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of navigation relationships is performed to identify microservice groups, then accuracy of entity grouping is improved, but time consumption and labor complexity increase exponentially
Solution Approach 1:
The patent replaces manual mechanical analysis of navigation relationships with an automated computer system that uses graph theory and clustering algorithms. The system automatically constructs navigation relationship graphs and applies spectral clustering to identify microservice groups, eliminating the need for manual examination of hundreds to thousands of entities and their relationships.
Solution Approach 2:
The patent introduces an intermediary computer system that acts as a mediator between the monolithic application and the microservice identification process. This intermediary system automatically analyzes navigation relationships, constructs graphs, applies clustering algorithms, and presents refined groupings, thereby reducing the cognitive load and time required for manual analysis.
2Quantity of substance
If comprehensive analysis of all navigation relationships is performed, then completeness of entity grouping is improved, but system complexity and difficulty of detection increase
Solution Approach 1:
The patent segments the complex task of analyzing all navigation relationships into manageable components: (1) automatic construction of navigation relationship graphs, (2) application of spectral clustering algorithms, and (3) presentation of refined groupings. This segmentation allows comprehensive analysis of all entities while maintaining manageable system complexity through automated processing.
Solution Approach 2:
The patent changes the parameters of analysis by transforming navigation relationships into graph theory representations and applying spectral clustering algorithms. This parameter transformation enables comprehensive analysis of all entities and their relationships while managing complexity through mathematical formulations and automated computation.
3Productivity
If automated clustering algorithms are applied to all Web service entities, then productivity of microservice identification is improved, but measurement precision and accuracy of grouping may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the system presents refined groupings of entities for review, and the navigation relationship graph is continuously updated and refined based on analysis. This feedback loop ensures that automated clustering produces accurate groupings by allowing verification and adjustment of results.
Solution Approach 2:
The patent performs preliminary actions by automatically constructing navigation relationship graphs and pre-processing entity data before applying clustering algorithms. This preliminary preparation ensures that the subsequent clustering operation works with optimized data structures, improving both productivity and the accuracy of the resulting groupings.
Data Source
AI summary
Systems and methods include identification of a plurality of entities of a monolithic application and navigation relationships between the plurality of entities, determination of a plurality of primary groups of entities based on the navigation relationships and, for each of the plurality of primary groups of entities, determination of a closeness of each entity of the primary group to each other entity of the primary group based on received queries of the navigation relationships between the entities of the primary group, and clustering of the entities of the primary group into two or more secondary groups based on the determined closeness of each entity of the primary group.