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Supporting the Decision of Migrating to Microservices Through Multi-layer Fuzzy Cognitive Maps

机译:通过多层模糊认知图支持迁移到微服务的决策

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Microservices architectures are gaining momentum for the development of applications as suites of small, autonomous, and conversational services, which are then easy to understand, deploy and scale. However, one of today's problems is that microservices introduce new complexities to the system and, despite the hype, many factors should be considered when deciding to adopt a microservices architecture. This paper proposes the first Decision Support System (DSS) to migrate to microservices, by identifying the key concepts and drivers regarding through a literature review and feedback from a group of experts from industry and academia. Then, these concepts are organized as a Multi-Layer Fuzzy Cognitive Map (ML-FCM), a graph-based computational intelligence model that captures the behavior of a given problem in nodes that represent knowledge in the domain, and offers the means to study their influence and interrelation. Static and dynamic analysis over the resulting ML-FCM helped us identify the prevailing drivers towards the migration to a microservices architecture.
机译:微服务架构作为小型,自主和对话式服务套件的开发应用程序正在获得发展的动力,这些套件随后易于理解,部署和扩展。但是,当今的问题之一是微服务为系统带来了新的复杂性,尽管大肆宣传,但在决定采用微服务体系结构时仍应考虑许多因素。本文提出了第一个决策支持系统(DSS),该方法将通过文献综述和来自行业和学术界的专家小组的反馈来确定关键概念和驱动因素,从而迁移到微服务。然后,将这些概念组织为多层模糊认知图(ML-FCM),这是一种基于图的计算智能模型,可捕获代表领域知识的节点中给定问题的行为,并提供研究方法它们的影响和相互关系。对生成的ML-FCM进行的静态和动态分析帮助我们确定了向微服务架构迁移的主要驱动因素。

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