Event-Driven Microservices Architecture for Scalable and Resilient Distributed Computing Systems
Keywords:
event-driven architecture, microservices, distributed systems, scalability, resilience, event sourcing, CQRS, saga coordination, message brokers, eventual consistency, fault isolation, backpressure, schema evolution, system leakage, cloud-native computingAbstract
Event-driven microservices are often sold as the natural answer to scalability, resilience, and distributed autonomy, yet the empirical record is less obedient than the architectural slogans suggest. The architecture replaces direct request dependency with asynchronous event exchange, private service ownership, broker-mediated communication, and delayed consistency, but the gain in decoupling usually reappears as operational debt in schema governance, observability, replay control, ordering ambiguity, poison-message handling, and cross-service accountability. Microservices are commonly defined through independent deployment and loose coupling, while event sourcing, sagas, CQRS, and CloudEvents formalize different parts of the event-driven stack; none of them removes the coordination problem, they merely relocate it. The evidence is contradictory at best.
References
[1] Amazon Web Services. (n.d.). Event sourcing pattern. AWS Prescriptive Guidance.
[2] Wadhwa, R. (2026). NoSQL migration and high-availability architecture. Computer Fraud & Security (CFS), 2026(1), 472–478.
[3] Cloud Native Computing Foundation. (2024). Cloud Native Computing Foundation announces the graduation of CloudEvents. Cloud Native Computing Foundation.
[4] Evans, E. (2003). Domain-driven design: Tackling complexity in the heart of software. Addison-Wesley.
[5] Wadhwa, R. (2026). Enterprise architecture at national scale: Transforming retail and financial infrastructure. Journal of Information Systems Engineering and Management, 11(1s), 1549–1559. https://doi.org/10.52783/jisem.v11i1s.14324
[6] Fowler, M. (2005). Event sourcing. MartinFowler.com.
[7] Fowler, M. (2015). Microservice trade-offs. MartinFowler.com.
[8] Wadhwa, R. (2026). Neutralizing “state-drift” in distributed retail: The mechanics of global event cascading. International Journal of Computational and Experimental Science and Engineering, 12(1), 928–934. https://doi.org/10.22399/ijcesen.4946
[9] Hohpe, G., & Woolf, B. (2003). Enterprise integration patterns: Designing, building, and deploying messaging solutions. Addison-Wesley.
[10] Kleppmann, M. (2017). Designing data-intensive applications: The big ideas behind reliable, scalable, and maintainable systems. O’Reilly Media.
[11] Wadhwa, R. (2026). Predictive workflow integrity in event-driven enterprise systems: Autonomous triage and geolocation-aware routing for large-scale resilience. Journal of Computational Analysis and Applications, 35(2), 90–97.
[12] Newman, S. (2021). Building microservices: Designing fine-grained systems (2nd ed.). O’Reilly Media.
[13] Wadhwa, R. (2025). A service-oriented data architecture for enterprise systems using event-driven microservices and distributed storage. IACSE – International Journal of Scientific Computing, 6(2), 7–19. https://doi.org/10.5281/zenodo.19734323
[14] Richardson, C. (2018). Microservices patterns: With examples in Java. Manning Publications.
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