With the maturity of web services, containers, and cloud computing
technologies, large services in traditional systems (e.g. the computation
services of machine learning and artificial intelligence) are gradually being
broken down into many microservices to increase service reusability and
flexibility. Therefore, this study proposes an efficiency analysis framework
based on queuing models to analyze the efficiency difference of breaking down
traditional large services into n microservices. For generalization, this study
considers different service time distributions (e.g. exponential distribution
of service time and fixed service time) and explores the system efficiency in
the worst-case and best-case scenarios through queuing models (i.e. M/M/1
queuing model and M/D/1 queuing model). In each experiment, it was shown that
the total time required for the original large service was higher than that
required for breaking it down into multiple microservices, so breaking it down
into multiple microservices can improve system efficiency. It can also be
observed that in the best-case scenario, the improvement effect becomes more
significant with an increase in arrival rate. However, in the worst-case
scenario, only slight improvement was achieved. This study found that breaking
down into multiple microservices can effectively improve system efficiency and
proved that when the computation time of the large service is evenly
distributed among multiple microservices, the best improvement effect can be
achieved. Therefore, this study's findings can serve as a reference guide for
future development of microservice architecture.

本研究提出了一种基于排队模型的效率分析框架，用于分析将传统的大型服务进行分解成 n 个微服务后的效率差异，并考虑了不同的服务时间分布和最坏和最好情况下的系统效率。实验结果表明，将大型服务分解成多个微服务可以提高系统效率，并证明当计算时间均匀分布在多个微服务之间时，可获得最佳的改进效果。