基于跟踪分析的微服务架构度量

Xin Peng, Chenxi Zhang, Zhongyuan Zhao, Akasaka Isami, Xiaofeng Guo, Yunna Cui
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引用次数: 2

摘要

微服务架构设计高度依赖专家经验,可能经常导致服务分解不当。此外,微服务架构可能会随着服务的不断发展而降级。因此,体系结构度量对于微服务体系结构的长期发展非常重要。由于服务的独立性和动态性,基于源代码分析的方法不能很好地捕获服务之间的交互。在本文中,我们提出了一种基于跟踪分析的微服务架构度量方法。我们为微服务架构度量定义了跟踪数据模型,它支持对请求的执行过程以及接口和服务之间的交互进行细粒度分析。基于数据模型,我们定义了14个体系结构指标来度量微服务系统的服务独立性和调用链复杂性。我们实施了该方法,并对一个学生课程项目、一个开源微服务基准系统和三个工业微服务系统进行了三个案例研究。结果表明,我们的方法可以很好地表征微服务架构的独立性和调用链的复杂性,并帮助开发人员识别由于不适当的服务分解和架构退化而导致的微服务架构问题。
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Trace analysis based microservice architecture measurement
Microservice architecture design highly relies on expert experience and may often result in improper service decomposition. Moreover, a microservice architecture is likely to degrade with the continuous evolution of services. Architecture measurement is thus important for the long-term evolution of microservice architectures. Due to the independent and dynamic nature of services, source code analysis based approaches cannot well capture the interactions between services. In this paper, we propose a trace analysis based microservice architecture measurement approach. We define a trace data model for microservice architecture measurement, which enables fine-grained analysis of the execution processes of requests and the interactions between interfaces and services. Based on the data model, we define 14 architectural metrics to measure the service independence and invocation chain complexity of a microservice system. We implement the approach and conduct three case studies with a student course project, an open-source microservice benchmark system, and three industrial microservice systems. The results show that our approach can well characterize the independence and invocation chain complexity of microservice architectures and help developers to identify microservice architecture issues caused by improper service decomposition and architecture degradation.
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