Microservice Extraction Based on a Comprehensive Evaluation of Logical Independence and Performance

• Published in: IEEE transactions on software engineering 2024-05, Vol.50 (5), p.1244-1263
• Main Authors: Ding, Zhijun, Xu, Yuehao, Feng, Binbin, Jiang, Changjun
• Format: Article
• Language: English
• Subjects: Algorithms; Architecture; Business; Costs; Data mining; Data transfer; Feature extraction; garbage collection; Heuristic methods; Microservice architectures; Microservices extraction; monolith decomposition; Performance evaluation; Principles; Workflow
• ISSN: 0098-5589; 1939-3520
• DOI: 10.1109/TSE.2024.3380194

Monolithic architectures are becoming increasingly difficult to cope with complex applications, and microservice architectures, which offer flexibility and logical independence in development and maintenance, are the new choice for companies and developers. Migrating a legacy monolithic architecture application to a microservice architecture rather than building it from scratch is considered an easy way to use it. To ensure that the migrated microservice applications can take advantage of their benefits, we need to propose a reasonable and effective microservice extraction method. Considering the single responsibility principle in the microservice design principle, most existing microservice extraction methods only pursue the high logical independence of the extraction results and pay little attention to whether the extraction results have good performance. Applications need to perform well, and studies have shown that poor microservice extraction schemes can negatively impact the performance of the migrated application. As a result, when extracting, we should also consider the performance of the results. A few studies consider the performance of extraction results, but only in terms of a few factors affecting performance, such as network overhead, rather than considering all factors affecting performance comprehensively, which leads to an inaccurate evaluation of performance. Therefore, oriented toward the most widely used managed languages today, we propose an effective Microservice Extraction method based on a Comprehensive Evaluation of logical independence and performance (MECE). Firstly, we propose a workflow-based approach to evaluate the performance of microservice extraction results by considering multiple influencing factors, focusing on the management cost ignored in existing studies, and designing an effective management cost evaluation model. After that, we propose a meta-heuristic search-based algorithm to obtain feasible microservice extraction results. In experiments based on actual deployments, the extraction results of the MECE method obtained a performance improvement of up to 46.15% without significant loss of logical independence compared to existing methods, which verifies the effectiveness of the method.