Microservice extraction based on knowledge graph from monolithic applications

被引:0
作者
Li, Zhiding [1 ]
Shang, Chenqi [1 ]
Wu, Jianjie [1 ]
Li, Yuan [2 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Software Engn, Wuhan, Hubei, Peoples R China
[2] Hubei Open Univ, Sch Elect & Informat Engn, Wuhan, Hubei, Peoples R China
关键词
Microservice extraction; Knowledge graph; Monolithic architecture; Constrained Louvain algorithm;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Context: Re-architecting monolithic systems with microservice architecture is a common trend. However, determining the "optimal" size of individual services during microservice extraction has been a challenge in software engineering. Common limitations of the literature include not being reasonable enough to be put into practical application; relying too much on human experience; neglection of the impact of hardware environment on the performance.Objective: To address these problems, this paper proposes a novel method based on knowledge-graph to support the extraction of microservices during the initial phases of re-architecting existing applications.Method: According to the microservice extraction method based on the AKF principle which is a widely practiced microservice design principle in the industry, four kinds of entities and four types of entity-entity relationships are designed and automatically extracted from specification and design artifacts of the monolithic application to build the knowledge graph. A constrained Louvain algorithm is proposed to identify microservice candidates.Results: Our approach is tested based on two open-source projects with the other three typical methods: the domain-driven design-based method, the similarity calculation-based method, and the graph clustering-based method . Conducted experiments show that our method performs well concerning all the evaluation metrics.
引用
收藏
页数:20
相关论文
共 50 条
  • [21] Enriching contextualized language model from knowledge graph for biomedical information extraction
    Fei, Hao
    Ren, Yafeng
    Zhang, Yue
    Ji, Donghong
    Liang, Xiaohui
    BRIEFINGS IN BIOINFORMATICS, 2021, 22 (03)
  • [22] Extraction of optimal synthesis conditions from scientific literature using a knowledge graph
    Kobayashi, Shigeru
    Kuwashiro, Norikazu
    Itoh, Fumiaki
    Sakurai, Dai
    Hitosugi, Taro
    SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS-METHODS, 2024, 4 (01):
  • [23] Automatic knowledge extraction from Chinese electronic medical records and rheumatoid arthritis knowledge graph construction
    Liu, Feifei
    Liu, Mingtong
    Li, Meiting
    Xin, Yuwei
    Gao, Dongping
    Wu, Jun
    Zhu, Jiaan
    QUANTITATIVE IMAGING IN MEDICINE AND SURGERY, 2023, 13 (06) : 3873 - +
  • [24] Attention-Aware Path-Based Relation Extraction for Medical Knowledge Graph
    Wen, Desi
    Liu, Yong
    Yuan, Kaiqi
    Si, Shangchun
    Shen, Ying
    SMART COMPUTING AND COMMUNICATION, SMARTCOM 2017, 2018, 10699 : 321 - 331
  • [25] Risk factors extraction and analysis of Chinese ship collision accidents based on knowledge graph
    Chen, Jihong
    Zhuang, Chenglin
    Shi, Jia
    Jiang, Houqiang
    Xu, Jinyu
    Liu, Jutong
    OCEAN ENGINEERING, 2025, 322
  • [26] Knowledge Graph Enhanced Event Extraction in Financial Documents
    Guo, Kaihao
    Jiang, Tianpei
    Zhang, Haipeng
    2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2020, : 1322 - 1329
  • [27] A Survey of Knowledge Graph Approaches and Applications in Education
    Qu, Kechen
    Li, Kam Cheong
    Wong, Billy T. M.
    Wu, Manfred M. F.
    Liu, Mengjin
    ELECTRONICS, 2024, 13 (13)
  • [28] Relationship Extraction and Processing for Knowledge Graph of Welding Manufacturing
    Guan, Kainan
    Du, Liang
    Yang, Xinhua
    IEEE ACCESS, 2022, 10 : 103089 - 103098
  • [29] International Workshop on Knowledge Graph: Heterogenous Graph Deep Learning and Applications
    Ding, Ying
    Arsintescu, Bogdan
    Chen, Ching-Hua
    Feng, Haoyun
    Scharffe, Francois
    Seneviratne, Oshani
    Sequeda, Juan
    KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, 2021, : 4121 - 4122
  • [30] Detecting Contradictions from CoAP RFC Based on Knowledge Graph
    Feng, Xinguo
    Zhang, Yanjun
    Meng, Mark Huasong
    Teo, Sin G.
    NETWORK AND SYSTEM SECURITY, NSS 2022, 2022, 13787 : 170 - 189