Penerapan Microservices dengan Docker Swarm pada Sistem Informasi Manajemen Rumah Sakit (SIMRS): Strategi Migrasi Monolith dan Evaluasi Performa
DOI:
https://doi.org/10.63447/jimik.v7i3.2040Keywords:
Docker, Docker Swarm, Hospital Information System, Microservices, Monolith MigrationAbstract
Digital transformation in Indonesia’s healthcare sector demands a Hospital Information System (SIMRS) that is scalable, reliable, and easy to integrate. Most existing SIMRS still rely on a monolithic architecture, making them vulnerable to single points of failure, hard to scale per module, and prone to downtime during updates. This study aims to examine strategies for migrating monolithic architectures to microservices and to evaluate the performance of a Docker- and Docker Swarm-based deployment on a SIMRS prototype consisting of twelve functional modules. The method combines a Systematic Literature Review on recent decomposition patterns with an experimental study on a two-node cluster running fourteen stacks and thirty-five services for a total of seventy-six active containers. Measurements show that the average HTTP response time ranges from 0.6 to 5.1 milliseconds for frontends (with a single billing-frontend outlier of 82.4 ms attributable to memory saturation) and from 0.8 to 3.0 milliseconds for backends, with per-container CPU utilization mostly below two percent and stable memory consumption within allocated limits. Docker Swarm proves effective in supporting high-availability replication, rolling updates, and automatic self-healing under normal operating conditions; one observed rolling-update failure (billing-backend dropping to 0/2 replicas until manual intervention) shows that reliable image-level health checks are a prerequisite for the promised zero-downtime behavior. The study concludes that microservices with Docker Swarm are a feasible adoption strategy for SIMRS in Indonesia, with manageable operational trade-offs.
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Ahmad, H., Treude, C., Wagner, M., & Szabo, C. (2024). Smart HPA: A resource-efficient horizontal pod auto-scaler for microservices architectures. Proceedings of the 2024 IEEE 21st International Conference on Software Architecture (ICSA).
Al-Debagy, O., & Martinek, P. (2018). A comparative review of microservices and monolithic architectures. IEEE 18th International Symposium on Computational Intelligence and Informatics (CINTI).
Andrade, B., Santos, S., & Silva, A. R. (2022). From monolith to microservices: Static and dynamic analysis comparison. arXiv preprint arXiv:2204.11844. https://arxiv.org/abs/2204.11844.
Arango, C., Dernat, R., & Sanabria, J. (2017). Performance evaluation of container-based virtualization for high-performance computing environments. arXiv preprint arXiv:1709.10140. https://arxiv.org/abs/1709.10140.
Borges, M. C., Bauer, J., Werner, S., Gebauer, M., & Tai, S. (2024). Informed and assessable observability design decisions in cloud-native microservice applications. IEEE International Conference on Software Architecture (ICSA).
Chaieb, M., & Saied, M. A. (2023). Automate migration to microservices architecture using machine learning techniques. arXiv preprint arXiv:2301.06508. https://arxiv.org/abs/2301.06508.
Dragoni, N., Dustdar, S., Larsen, S. T., & Mazzara, M. (2017). Microservices: Migration of a mission-critical system. IEEE Transactions on Services Computing.
Faustino, D., Gonçalves, N., Portela, M., & Silva, A. R. (2022). Stepwise migration of a monolith to a microservices architecture: Performance and migration effort evaluation. arXiv preprint arXiv:2201.07226. https://arxiv.org/abs/2201.07226.
Fritzsch, J., Bogner, J., Wagner, S., & Zimmermann, A. (2019). Microservices migration in industry: Intentions, strategies, and challenges. 35th IEEE International Conference on Software Maintenance and Evolution (ICSME).
Fritzsch, J., Bogner, J., Zimmermann, A., & Wagner, S. (2018). From monolith to microservices: A classification of refactoring approaches. DevOps 2018.
Kalia, A., Xiao, J., Krishna, R., Sinha, S., Vukovic, M., & Banerjee, D. (2021). Mono2Micro: A practical and effective tool for decomposing monolithic Java applications to microservices. Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE).
Levcovitz, A., Terra, R., & Valente, M. T. (2016). Towards a technique for extracting microservices from monolithic enterprise systems. arXiv preprint arXiv:1605.03175. https://arxiv.org/abs/1605.03175.
Medel, V., Tolosana-Calasanz, R., Bañares, J. Á., Arronategui, U., & Rana, O. F. (2018). Characterising resource management performance in Kubernetes. Computers & Electrical Engineering, 68, 286–297.
Mohottige, T. I., Polyvyanyy, A., Buyya, R., Fidge, C., & Barros, A. (2024). Microservices-based software systems reengineering: State-of-the-art and future directions. arXiv preprint arXiv:2407.13915. https://arxiv.org/abs/2407.13915.
Morabito, R. (2016). A performance evaluation of container technologies on Internet of Things devices. IEEE INFOCOM 2016 Workshop on Computer Communications.
Nogueira, V. L., Felizardo, F. S., Amaral, A. M. M. M., Assunção, W. K. G., & Colanzi, T. E. (2024). Insights on microservice architecture through the eyes of industry practitioners. arXiv preprint arXiv:2408.10434. https://arxiv.org/abs/2408.10434.
Putra, B. P., Satria, B., Murni, A., Surya, C., & Sakinah, P. (2024). Implementasi metode waterfall dan system usability scale testing pada aplikasi fisioterapi pasien BPJS. INTI Nusa Mandiri, 19(1), 31–39. https://doi.org/10.33480/inti.v19i1.5571.
Ristova, I., & Stoico, V. (2026). An empirical study on how architectural topology affects microservice performance and energy usage. arXiv preprint arXiv:2604.00080. https://arxiv.org/abs/2604.00080.
Rodriguez, M. A., & Buyya, R. (2019). Container-based cluster orchestration systems: A taxonomy and future directions. arXiv preprint arXiv:1807.06193. https://arxiv.org/abs/1807.06193.
Saha, P., Beltre, A., Uminski, P., & Govindaraju, M. (2018). Evaluation of Docker containers for scientific workloads in the cloud. Proceedings of the Practice and Experience on Advanced Research Computing (PEARC).
Saputro, P. R., & Aziza, R. F. A. (2025). PWA and non-PWA performance analysis: Chrome extension testing on e-commerce platform. Jurnal Ilmu Pengetahuan dan Teknologi Komputer, 10(4), 909–916. https://doi.org/10.33480/jitk.v10i4.6238.
Shamim, S. I., Gibson, J. A., Morrison, P., & Rahman, A. (2022). Benefits, challenges, and research topics: A multi-vocal literature review of Kubernetes. arXiv preprint arXiv:2211.07032. https://arxiv.org/abs/2211.07032.
Truyen, E., Van Landuyt, D., Preuveneers, D., Lagaisse, B., & Joosen, W. (2020). A comprehensive feature comparison study of open-source container orchestration frameworks (Docker Swarm, Kubernetes, Mesos). arXiv preprint arXiv:2002.02806. https://arxiv.org/abs/2002.02806.
Waseem, M., Liang, P., & Shahin, M. (2020). A systematic mapping study on microservices architecture in DevOps. Journal of Systems and Software.
Xiao, X., Gao, C., & Bogner, J. (2025). On the effectiveness of microservices tactics and patterns to reduce energy consumption: An experimental study on trade-offs. arXiv preprint arXiv:2501.14402. https://arxiv.org/abs/2501.14402.
Zhang, S., Xia, S., Fan, W., Shi, B., Xiong, X., Zhong, Z., Ma, M., Sun, Y., & Pei, D. (2024). Failure diagnosis in microservice systems: A comprehensive survey and analysis. arXiv preprint arXiv:2407.01710. https://arxiv.org/abs/2407.01710.
Zhao, Y., De Matteis, T., & Bogner, J. (2025). How does microservice granularity impact energy consumption and performance? A controlled experiment. arXiv preprint arXiv:2502.00482. https://arxiv.org/abs/2502.00482.
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Copyright (c) 2026 Farizal Ginanjar, Rizqia Fauziah Rachma, Wahid Diyono, Mustawi Farhan, Windu Gata
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