A Cloud-Native Microservice Architecture for Scalable Real-Time Geohazard Monitoring: An Assessment of Predictive Model Insufficiency Amidst Increasing Seismic Events

Dr. Elias R. Vance; Prof. Coraline Q. Harthwick

Open Access

A Cloud-Native Microservice Architecture for Scalable Real-Time Geohazard Monitoring: An Assessment of Predictive Model Insufficiency Amidst Increasing Seismic Events

PDF

Dr. Elias R. Vance ¹ , Prof. Coraline Q. Harthwick ¹ ,

⁴ Department of Cloud Computing and Distributed Systems, Aethelred University, Edinburgh, United Kingdom

⁴ Faculty of Applied Geophysics and Environmental Modeling, Institute of Oceanic Sciences, Melbourne, Australia

Abstract

The growing frequency and intensity of seismic events have underscored the need for robust, scalable, and real-time geohazard monitoring systems. This study proposes a cloud-native microservice architecture designed to address the performance limitations of conventional monolithic models in seismic data acquisition, processing, and prediction. The architecture leverages containerized services, distributed data pipelines, and event-driven frameworks to ensure elasticity, resilience, and low-latency communication across geospatial sensor networks. Real-time analytics were performed using streaming platforms integrated with machine learning inference modules for anomaly detection and early warning dissemination. However, the assessment reveals predictive model insufficiency when dealing with rapidly escalating seismic activities and incomplete sensor data, highlighting the constraints of existing training datasets and static learning paradigms. Experimental evaluations on simulated and live geohazard data streams demonstrate that the proposed framework significantly improves throughput and fault tolerance while maintaining near-real-time responsiveness. The findings emphasize the critical need for adaptive and self-learning predictive models within cloud-native architectures to enhance future seismic hazard forecasting accuracy and operational scalability.

Keywords

Microservice Architecture, Cloud-Native Computing, Seismic Activity, Geohazard Monitoring, Scalability Patterns, Predictive Modeling, Sea Level Rise

References

📄 Wais, A. (2021). Optimizing container elasticity for microservices in hybrid clouds (Doctoral dissertation, Wien).

📄 Hariharan, R. (2025). Zero trust security in multi-tenant cloud environments. Journal of Information Systems Engineering and Management, 10(45s). https://doi.org/10.52783/jisem.v10i45s.8899

📄 Bogner, J., Fritzsch, J., Wagner, S., & Zimmermann, A. (2021). Industry practices and challenges for the evolvability assurance of microservices: An interview study and systematic grey literature review. Empirical Software Engineering, 26, 1–39.

📄 Koneru, N. M. K. (2025). Containerization best practices: Using Docker and Kubernetes for enterprise applications. Journal of Information Systems Engineering and Management, 10(45s), 724–743. https://doi.org/10.55278/jisem.2025.10.45s.724

📄 Christudas, B. (209). Microservices Architecture. In Practical Microservices Architectural Patterns: Event-Based Java Microservices with Spring Boot and Spring Cloud (pp. 55–86).

📄 Camilli, M., Guerriero, A., Janes, A., Russo, B., & Russo, S. (2022, May). Microservices integrated performance and reliability testing. In Proceedings of the 3rd ACM/IEEE International Conference on Automation of Software Test (pp. 29–39).

📄 Srivastava, R. (2021). Cloud Native Microservices with Spring and Kubernetes: Design and Build Modern Cloud Native Applications Using Spring and Kubernetes (English Edition). BPB Publications.

📄 Mahajan, A., Gupta, M. K., & Sundar, S. (2018). Cloud-Native Applications in Java: Build Microservice-Based Cloud-Native Applications that Dynamically Scale. Packt Publishing Ltd.

📄 Klinaku, F., Frank, M., & Becker, S. (2018, April). CAUS: An elasticity controller for a containerized microservice. In Companion of the 2018 ACM/SPEC International Conference on Performance Engineering (pp. 93–98).

📄 Söylemez, M., Tekinerdogan, B., & Kolukısa Tarhan, A. (2022). Challenges and solution directions of microservice architectures: A systematic literature review. Applied Sciences, 12(11), 5507.

📄 Chadha, K. S. (2025). Zero-trust data architecture for multi-hospital research: HIPAA-compliant unification of EHRs, wearable streams, and clinical trial analytics. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3477

📄 Rasheedh, J. A., & Saradha, S. (2022). Design and development of resilient microservices architecture for cloud-based applications using hybrid design patterns.

📄 Davis, C. (2019). Cloud Native Patterns: Designing Change-Tolerant Software. Simon & Schuster.

📄 Patel, D. B. (2025). Comparing neural networks and traditional algorithms in fraud detection. The American Journal of Applied Sciences, 7(7), 128–132. https://doi.org/10.37547/tajas/Volume07Issue07-13

📄 Chen, L. (2018, April). Microservices: Architecting for continuous delivery and DevOps. In 2018 IEEE International Conference on Software Architecture (ICSA) (pp. 39–397). IEEE.

📄 Koschel, A., Hausotter, A., Lange, M., & Gottwald, S. (2020). Keep it in Sync! Consistency Approaches for Microservices—An Insurance Case Study. In SERVICE COMPUTATION 2020: The Twelfth International Conference on Advanced Service Computing (pp. 7–14).

📄 Bonthu, C., Kumar, A., & Goel, G. (2025). Impact of AI and machine learning on master data management. Journal of Information Systems Engineering and Management, 10(32s), 46–62. https://doi.org/10.55278/jisem.2025.10.32s.46

📄 Siqueira, F., & Davis, J. G. (2021). Service computing for industry 4.0: State of the art, challenges, and research opportunities. ACM Computing Surveys (CSUR), 54(9), 1–38.

📄 Gannon, D., Barga, R., & Sundaresan, N. (2017). Cloud-native applications. IEEE Cloud Computing, 4(5), 16–21.

📄 Balalaie, A., Heydarnoori, A., & Jamshidi, P. (2016). Migrating to cloud-native architectures using microservices: An experience report. In Advances in Service-Oriented and Cloud Computing (pp. 201–215). Springer International Publishing.

📄 Wang, S., Ding, Z., & Jiang, C. (2020). Elastic scheduling for microservice applications in clouds. IEEE Transactions on Parallel and Distributed Systems, 32(1), 98–115.

📄 Aksakalli, I. K., Çelik, T., Can, A. B., & Tekinerdoğan, B. (2021). Deployment and communication patterns in microservice architectures: A systematic literature review. Journal of Systems and Software, 180, 111014.

📄 Raj, P., Vanga, S., & Chaudhary, A. (2022). Cloud-Native Computing: How to Design, Develop, and Secure Microservices and Event-Driven Applications. John Wiley & Sons.

📄 Laszewski, T., Arora, K., Farr, E., & Zonooz, P. (2018). Cloud Native Architectures: Design High-Availability and Cost-Effective Applications for the Cloud. Packt Publishing Ltd.

📄 Sayyed, Z. (2025). Application-level scalable leader selection algorithm for distributed systems. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3856

📄 Balalaie, A., Heydarnoori, A., Jamshidi, P., Tamburri, D. A., & Lynn, T. (2018). Microservices migration patterns. Software: Practice and Experience, 48(11), 2019–2042.

📄 Henning, S., & Hasselbring, W. (2022). A configurable method for benchmarking scalability of cloud-native applications. Empirical Software Engineering, 27(6), 143.

📄 Torkura, K. A., Sukmana, M. I., Cheng, F., & Meinel, C. (2017, November). Leveraging cloud native design patterns for security-as-a-service applications. In 2017 IEEE International Conference on Smart Cloud (SmartCloud) (pp. 90–97). IEEE.

📄 Toffetti, G., Brunner, S., Blöchlinger, M., Spillner, J., & Bohnert, T. M. (2017). Self-managing cloud-native applications: Design, implementation, and experience. Future Generation Computer Systems, 72, 165–179.

📄 Banijamali, A., Jamshidi, P., Kuvaja, P., & Oivo, M. (2019, November). Kuksa: A cloud-native architecture for enabling continuous delivery in the automotive domain. In International Conference on Product-Focused Software Process Improvement (pp. 455–472). Springer.

📄 Gilbert, J. (2018). Cloud Native Development Patterns and Best Practices: Practical Architectural Patterns for Building Modern, Distributed Cloud-Native Systems. Packt Publishing Ltd.

📄 Fourati, M. H., Marzouk, S., & Jmaiel, M. (2022). Epma: Elastic platform for microservices-based applications: Towards optimal resource elasticity. Journal of Grid Computing, 20(1), 6.

📄 Chandra, R., Lulla, K., & Sirigiri, K. (2025). Automation frameworks for end-to-end testing of large language models (LLMs). Journal of Information Systems Engineering and Management, 10(43s), e464–e472. https://doi.org/10.55278/jisem.2025.10.43s.8400

📄 Waseem, M., Liang, P., Shahin, M., Di Salle, A., & Márquez, G. (2021). Design, monitoring, and testing of microservices systems: The practitioners’ perspective. Journal of Systems and Software, 182, 111061.

📄 Indrasiri, K., & Suhothayan, S. (2021). Design Patterns for Cloud Native Applications. O’Reilly Media, Inc.

📄 Torkura, K. A., Sukmana, M. I., & Meinel, C. (2017, December). Integrating continuous security assessments in microservices and cloud-native applications. In Proceedings of the 10th International Conference on Utility and Cloud Computing (pp. 171–180).

📄 Telang, T. (2022). Cloud-native application development. In Beginning Cloud Native Development with MicroProfile, Jakarta EE, and Kubernetes (pp. 29–54). Apress.

📄 Štefanič, P., Cigale, M., Jones, A. C., Knight, L., Taylor, I., Istrate, C., … & Zhao, Z. (2019). SWITCH workbench: A novel approach for the development and deployment of time-critical microservice-based cloud-native applications. Future Generation Computer Systems, 99, 197–212.

📄 Zhao, P., Wang, P., Yang, X., & Lin, J. (2020). Towards cost-efficient edge intelligent computing with elastic deployment of container-based microservices. IEEE Access, 8, 102947.

📄 De Nardin, I. F., da Rosa Righi, R., Lopes, T. R. L., da Costa, C. A., Yeom, H. Y., & Köstler, H. (2021). On revisiting energy and performance in microservices applications: A cloud elasticity-driven approach. Parallel Computing, 1018, 102858.

📄 Sardana, J., & Reddy Dhanagari, M. (2025). Bridging IoT and healthcare: Secure, real-time data exchange with Aerospike and Salesforce Marketing Cloud. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3853

📄 Fritzsch, J., Bogner, J., Wagner, S., & Zimmermann, A. (2019, September). Microservices migration in industry: Intentions, strategies, and challenges. In 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) (pp. 481–490). IEEE.

📄 Garrison, J., & Nova, K. (2017). Cloud Native Infrastructure: Patterns for Scalable Infrastructure and Applications in a Dynamic Environment. O’Reilly Media.

📄 Pandiya, D. K. (2021). Scalability patterns for microservices architecture. Educational Administration: Theory and Practice, 27(3), 1178–1183.

📄 Reddy Gundla, S. (2025). PostgreSQL tuning for cloud-native Java: Connection pooling vs. reactive drivers. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3479

📄 Gannavarapu, P. (2025). Performance optimization of hybrid Azure AD join across multi-forest deployments. Journal of Information Systems Engineering and Management, 10(45s), e575–e593. https://doi.org/10.55278/jisem.2025.10.45s.575

📄 Kratzke, N., & Siegfried, R. (2021). Towards cloud-native simulations—Lessons learned from the front-line of cloud computing. The Journal of Defense Modeling and Simulation, 18(1), 39–58.

📄 Ghani, I., Wan-Kadir, W. M., Mustafa, A., & Babir, M. I. (2019). Microservice testing approaches: A systematic literature review. International Journal of Integrated Engineering, 11(8), 65–80.

📄 Zhang, S., Pandey, A., Luo, X., Powell, M., Banerji, R., Fan, L., … & Luzcando, E. (2022). Practical adoption of cloud computing in power systems—Drivers, challenges, guidance, and real-world use cases. IEEE Transactions on Smart Grid, 13(3), 2390–2411.

International Research Journal of Advanced Engineering and Technology

A Cloud-Native Microservice Architecture for Scalable Real-Time Geohazard Monitoring: An Assessment of Predictive Model Insufficiency Amidst Increasing Seismic Events

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles