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arXiv 2402.08481cs.SE
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向微服务迁移:分解策略与分析指标的对比研究

Migration to Microservices: A Comparative Study of Decomposition Strategies and Analysis Metrics

Meryam chaieb, Mohamed Aymen Saied

AI总结:

本文提出一种基于密度的聚类方法,通过考虑类之间的静态分析、结构和语义关系来识别单体应用中的潜在微服务,并在多个应用和基线上验证了其有效性和稳定性。

摘要:

微服务架构风格因其可扩展性、可重用性和易于维护性而广受青睐,促使开发人员越来越多地采用它。然而,从单体架构向基于微服务的架构过渡是复杂且昂。为此,我们提出了一种利用聚类来识别给定单体应用中潜在微服务的新方法。我们的方法采用基于密度的聚类算法,考虑了类之间的静态分析、结构和语义关系,确保了功能和上下文连贯的划分。为了评估我们微服务建议方法的可靠性,我们对超参数敏感性进行了深入分析,并将其与两种成熟的聚类算法进行了比较。一项全面的比较分析涉及七个应用,利用四个开源 Java 项目的数据集,对照六个基线进行评估。评估指标衡量了所生成微服务的质量。此外,我们还将建议的微服务与三个基于微服务的应用中手动识别的微服务进行了细致比较。这一比较为我们方法的效能和可靠性提供了细致的理解。我们的方法展示了有前景的结果,表现出卓越的有效性和令人称赞的稳定性。

英文摘要:

The microservices architectural style is widely favored for its scalability, reusability, and easy maintainability, prompting increased adoption by developers. However, transitioning from a monolithic to a microservices-based architecture is intricate and costly. In response, we present a novel method utilizing clustering to identify potential microservices in a given monolithic application. Our approach employs a density-based clustering algorithm considering static analysis, structural, and semantic relationships between classes, ensuring a functionally and contextually coherent partitioning. To assess the reliability of our microservice suggestion approach, we conducted an in-depth analysis of hyperparameter sensitivity and compared it with two established clustering algorithms. A comprehensive comparative analysis involved seven applications, evaluating against six baselines, utilizing a dataset of four open-source Java projects. Metrics assessed the quality of generated microservices. Furthermore, we meticulously compared our suggested microservices with manually identified ones in three microservices-based applications. This comparison provided a nuanced understanding of our approach's efficacy and reliability. Our methodology demonstrated promising outcomes, showcasing remarkable effectiveness and commendable stability.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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