2 research outputs found
Research on Efficiency Analysis of Microservices
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.Comment: in Chinese languag