In view of problems of traditional service design, such as high coupling, low transparency and complex change, a dynamic microservice interaction platform design based on stream engine is proposed. Service process is decomposed into fine-grained microservice models whose boundaries are defined in a unified model representation, so that a service can be implemented independently of the interfaces of other services. Microservices are connected through stream channels. Service encapsulation is carried out on the producer side based on temporal and spatial features of data to construct unified information representation. Service analysis is carried out on the consumer side to divide and reorganize data. A complete service process for business process is constructed, driven by streaming data. Based on this design, a visual microservice interaction management platform is realized and applied to spinning detection process of engine manufacturing. Compared with traditional service systems, this platform design features in lower coupling, more flexibility in service change, expansion and evolution, and improved performance in service monitoring and fault handling.
YIN Yifan, XU Kaizhou, WANG Yanhua, ZHOU Xin, CAI Hongming
. Dynamic Microservice Interaction Platform Design Based on Stream Engine[J]. Journal of Applied Sciences, 2021
, 39(4)
: 521
-531
.
DOI: 10.3969/j.issn.0255-8297.2021.04.001
[1] Zhao Q Y, Wei L, Sheng Z W, et al. Research on service-oriented application support software platform of manufacturing informatization[C]//IEEE International Conference on Computer & Communications, 2016:1402-1406.
[2] 延建林, 孔德婧. 解析"工业互联网" 与"工业4.0" 及其对中国制造业发展的启示[J]. 中国工程科学, 2015, 17(7):141-144. Yan J L, Kong D J. Study on "industrial Internet" and "industry 4.0"[J]. Strategic Study of Chinese Academy of Engineering, 2015, 17(7):141-144. (in Chinese)
[3] Tao F, Sui F, Liu A, et al. Digital twin-driven product design framework[J]. International Journal of Production Research, 2019, 57(12):3935-3953.
[4] 张曙. 工业4.0和智能制造[J]. 机械设计与制造工程, 2014(8):1-5. Zhang S. Industry 4.0 and smart manufacturing[J]. Machine Design and Manufacturing Engineering, 2014(8):1-5. (in Chinese)
[5] Siderska J, Jadaan K S. Cloud manufacturing:a service-oriented manufacturing paradigm. A review paper[J]. Engineering Management in Production and Services, 2018, 10(1):22-31.
[6] Aladwan F, Alzghoul A, Ali E M M, et al. Service composition in service oriented architecture:a survey[J]. Modern Applied Science, 2018, 12(12):18-27.
[7] Francesco P D, Lago P, Malavolta I. Migrating towards microservice architectures:an industrial survey[C]//2018 IEEE International Conference on Software Architecture, 2018:29-38.
[8] Abdullah M, Iqbal W, Erradi A. Unsupervised learning approach for Web application auto-decomposition into microservices[J]. Journal of Systems and Software, 2019, 151:243-257.
[9] Kookarinrat P, Temtanapat Y. Design and implementation of a decentralized message bus for microservices[C]//International Joint Conference on Computer Science & Software Engineering, 2016:1-6.
[10] Schuler R, Kesselman C, Czajkowski K. Data centric discovery with a data-oriented architecture[C]//Workshop on the Science of Cyberinfrastructure Research, 2015:37-44.
[11] Fang Z, Computer D O. Mass data mining system for Internet of things based on association rules Apriori algorithm[J]. Journal of Hebei North University (Natural Science Edition), 2015:15-18.
[12] Boubiche S, Boubiche D E, Bilami A, et al. Big data challenges and data aggregation strategies in wireless sensor networks[J]. IEEE Access, 2018, 6:20558-20571.
[13] Begoli E, Camacho-Rodriguez J, Hyde J, et al. Apache calcite:a foundational framework for optimized query processing over heterogeneous data sources[C]//Proceedings of the 2018 International Conference on Management of Data, 2018:221-230.
[14] Shi X, Chen Z, Wang H, et al. Convolutional LSTM network:a machine learning approach for precipitation nowcasting[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015, 1:802-810.
[15] Goyal H, Sharma C, Joshi N. An integrated approach of GIS and spatial data mining in big data[J]. International Journal of Computer Applications, 2017, 169(11):1-6.
[16] Li L, Zhang J, Wang Y, et al. Missing value imputation for traffic-related time series data based on a multi-view learning method[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(8):2933-2943.
[17] 段红超. 基于SSH框架的MES生产制造管理系统设计与实现[D]. 杭州:浙江工业大学, 2017:1-60.
[18] 高媛媛. 基于SOA架构的生产管理系统的研发[D]. 北京:北京工业大学, 2018:1-86.