MSA-Lab：模型驱动的微服务集成设计平台

从单体系统迁移到微服务系统是当前业界对遗留系统实施再工程化的主流选项之一,基于单体遗留系统的微服务体系架构重构则是实现该迁移的关键步骤.目前学界多集中在微服务识别方法的研究上;业界虽有许多面向微服务架构的遗留系统重构的实践,但缺乏系统性的方法及高效鲁棒的工具.鉴于此,在微服务识别与模型驱动开发方法前期研究的基础上,研发一种模型驱动的、可用于单体遗留系统微服务化重构的集成设计平台MSA-Lab.它通过分析单体遗留系统运行日志中的方法调用序列,对其中的类和数据表进行类型识别和聚类以构造抽象微服务,同时生成包括微服务图和微服务序列图在内的系统架构设计模型.它包括用于微服务自动识别与设计模型自动生成的核心部件MSA-Generator,以及用于微服务静态结构模型与动态行为模型可视化展现、交互式建模、模型语法约束检验的核心部件MSA-Modeller.在MSA-Lab平台上,通过对4个开源项目实施有效性、鲁棒性、功能转换完备性等实验以及对3个同类型工具实施性能对比实验,结果表明:所提平台拥有很好的有效性、鲁棒性及实现面向日志的功能转换完备性,且性能更加优越.

MSA-Lab: Integrated Design Platform for Model-driven Development of Microservices

Migrating from monolithic systems to microservice systems is one of the mainstream options for the industry to realize the reengineering of legacy systems, and microservice architecture refactoring based on monolithic legacy systems is the key to realizing migration. Currently, academia mainly focuses on the research on microservice identification methods, and there are many industry practices of legacy systems refactored into microservices. However, systematic approaches and efficient and robust tools are insufficient. Therefore, based on earlier research on microservices identification and model-driven development method, this study presents MSA-Lab, an integrated design platform for microservice refactoring of monolithic legacy systems based on the model-driven development approach. MSA-Lab analyzes the method call sequence in the running log of the monolithic legacy system, identifies and clusters classes and data tables for constructing abstract microservices, and generates a system architecture design model including the microservice diagram and microservice sequence diagram. The model has two core components: MSA-Generator for automatic microservice identification and design model generation and MSA-Modeller for visualization, interactive modeling, and model syntax constraint checking of microservice static structure and dynamic behavior models. This study conducts experiments in the MSA-Lab platform for effectiveness, robustness, and function transformation completeness on four open-source projects and carries out performance comparison experiments with three same-type tools. The results show that the platform has excellent effectiveness and robustness, function transform completeness for running logs, and superior performance.