Towards Migrating Legacy Software Systems to Microservice-based Architectures: a Data-Centric Process for Microservice Identification

被引:5
|
作者
Romani, Yamina [1 ]
Tibermacine, Okba [1 ]
Tibermacine, Chouki [2 ]
机构
[1] Univ Biskra, Comp Sci Dept, Biskra, Algeria
[2] Univ Montpellier, CNRS, LIRMM, Montpellier, France
关键词
Microservices; Database-per-service pattern; Software Architecture; monolithic to microservice migration; topic identification; clustering;
D O I
10.1109/ICSA-C54293.2022.00010
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
"Microservice-based architecture" is an architectural style exploited to develop software systems with the main concern of independent maintainability, deployability and scalability. These important capabilities in modern software development and operation settings led many companies to migrate their existing (legacy) monolithic software systems towards microservice-based architectures. The migration process is a challenging task. It requires splitting the system into consistent parts that represent the set of microservices. Existing works focus mainly on functional aspects in this splitting. We argue in this work that it would be beneficial to start this splitting by decomposing the database into clusters, where the data in each cluster is associated to a microservice's own independent database. This is commonly known as the "database-per-service" pattern in microservice architectures. This paper proposes our preliminary work on a data-centric process to identify microservices. This process performs database schema analysis and clustering in order to make topic identification. It aims at identifying a set of topics which correspond to potential microservices.
引用
收藏
页码:15 / 19
页数:5
相关论文
共 21 条
  • [21] Advancing deep learning-based acoustic leak detection methods towards application for water distribution systems from a data-centric perspective( vol 261, 121999, 2024)
    Wu, Yipeng
    Ma, Xingke
    Guo, Guancheng
    Jia, Tianlong
    Huang, Yujun
    Liu, Shuming
    Fan, Jingjing
    Wu, Xue
    WATER RESEARCH, 2024, 262