The rapid advancement of technology and increasing market competition compel businesses to adapt swiftly by implementing new features and services to meet user demands. Traditional monolithic software architectures often hinder this agility due to challenges in scalability and maintenance. This article aims to analyze microservice architecture to solve these challenges, exploring its historical development, current trends, practical implementation aspects, and comparison with alternative architectural styles such as modulith architecture. The study examines the limitations of monolithic architectures in handling growing complexity and scaling requirements. It explores the emergence of microservice architecture, highlighting core characteristics like independent services, decentralized data management, and autonomous deployment. The evolution influenced by agile methodologies and DevOps practices is discussed. A comparative analysis with other architectural styles—including monolithic, service-oriented, modular monolithic, and serverless architectures—identifies contexts where microservices are most beneficial. The research reviews essential tools and technologies for implementing microservices, such as Docker for containerization, Kubernetes for orchestration, and service meshes like Istio and Linkerd. Practical cases from industry leaders like Netflix and Amazon illustrate successful adoption and the challenges faced during implementation. Findings indicate that while microservice architecture offers significant scalability, flexibility, and rapid deployment advantages, it also introduces complexities related to distributed system management and security. The study emphasizes the importance of adopting best practices and standards, such as those promoted by the Cloud Native Computing Foundation and utilizing modern tools to mitigate these challenges. For organizations where full microservices adoption may be impractical, modulith architecture is a viable alternative that combines modularity with deployment simplicity. The article concludes that the choice of architecture should be carefully aligned with the project's specific needs, resources, and long-term strategic goals.

microservice architecture, containerization, scalability, orchestration, distributed systems, DevOps

Full Text:

PDF

1. It's 2024, how many cars are there in the world? WHICHCAR : веб-сайт. URL: https://www.whichcar.com.au/news/how-many-cars-are-there-in-the-world (date of application 12.10.2024) [in English].

2. The different types of engine. Febiac : веб-сайт. URL: https://www.febiac.be/en/article/the-different-types-of-engine (date of application 12.10.2024) [in English].

3. Apostolos Giannoulidis, Anastasios Gounaris, Ioannis Constantinou. Exploring unsupervised anomaly detection for vehicle predictive maintenance with partial information. Proceedings of the 27th International Conference on Extending Database Technology (EDBT). 2024. [in English].

4. Sandeep Nair Narayanan, Sudip Mittal & Anupam Joshi. IEEE Workshop on Smart Service Systems. May 2016. [in English].

5. Iskandar, Karto & Tambayong, Alfred & Mulya, Muhammad & Elfanlie, Steven & Herlina, Maria. Mobile-Based Car Diagnostic Application Using Onboard Diagnostic-II Scanner. ComTech Computer Mathematics and Engineering Applications 14(2):P 129-141. 2023. DOI: 10.21512/comtech.v14i2.9138 [in English].

6. Aulin V.V., & Panarin D.Ie. (2014). Kompleksnyi pidkhid v optymizatsii ta planuvanni protsesiv ekspluatatsii remontu avtomobiliv z vykorystanniam telemetrychnykh system dystantsiinoi elektronnoi diahnostyky [A comprehensive approach in optimizing and planning the operation processes of car repair using telemetry systems of remote electronic diagnostics]. Visnyk Zhytomyrskoho derzhavnoho tekhnolohichnoho universytetu. Seriia : Tekhnichni nauky - Bulletin of the Zhytomyr State Technological University. Series: Technical sciences. № 2. P. 29-32 [in Ukrainian].

7. Aulin V.V., & Panarin D.Ie. (2014). Udoskonalennia protsesu tekhnichnoho obsluhovuvannia avtomobiliv z vykorystanniam metodiv dystantsiinoi elektronnoi diahnostyky [Improving the process of car maintenance using methods of remote electronic diagnostics]. Visnyk Vinnytskoho politekhnichnoho instytutu - Bulletin of the Vinnytsia Polytechnic Institute. № 4. P. 88-91 [in Ukrainian].

8. Aulin V.V., Hrynkiv A.V., Nadych T.M., & Yatsenko V.Iu. (2024). Zastosuvanni zasobiv dystantsiinoi diahnostyky dlia pidvyshchennia efektyvnosti tekhnichnoi ekspluatatsii mobilnykh mashyn [Application of remote diagnostics to improve the efficiency of technical operation of mobile machines]. Zb. tez dopovidei II Mizhnarodnoi naukovo-tekhnichnoi konferentsii «Kramarovski chytannia» - Coll. Abstracts of reports of the 11th International Scientific and Technical Conference "Kramor's Readings". 22-23 liut. 2024 r., m. Kyiv. - K. : Vydavnychyi tsentr NUBiP Ukrainy. P. 100-103 [in Ukrainian].

9. What is Vehicle CAN bus and why do you need to care. Earth2 digital : веб-сайт. URL: https://www.earth2.digital/blog/what-is-vehicle-can-bus-ecu-evoque-adam-ali.html (date of application 13.10.2024) [in English].

10. OBD II Protocols Explained. OBD Experts : веб-сайт. URL: https://www.obdexperts.com/obd-ii-protocols-explained/ (date of application 13.10.2024) [in English].

11. OBD2 Trouble Codes. OBD Codes : веб-сайт. URL: https://www.obd-codes.com/trouble_codes/ (date of application 13.10.2024).

12. Reconstruction based anomaly detection with Autoencoder. Github : веб-сайт. URL: https://github.com/Zinwaiyan274/Reconstruction-based-anomaly-detection-with-Autoencoder (date of application 15.10.2024) [in English].

13. Chalapathy, Raghavendra & Toth, Edward & Chawla, Sanjay. Group Anomaly Detection Using Deep Generative Models: Recognizing Outstanding Ph.D. Research. Energy Transfer Processes in Polynuclear Lanthanide Complexes (pp.173-189). 2019. DOI: 10.1007/978-3-030-10925-7_11 [in English].