Engineering Cloud-Native Microservices in Java: A Scalable Approach for Modern Enterprise Software Architectures
Abstract
As enterprises undergo digital transformation, the need for scalable, maintainable, and resilient software architectures has become critical. Cloud-native microservices offer a solution by decomposing complex systems into independently deployable services that align with business capabilities. Java, long established in enterprise environments, has evolved through modern frameworks such as Spring Boot, Quarkus, and Micronaut to support this architectural shift. This research presents a structured approach to engineering Java-based cloud-native microservices. It combines best practices in service decomposition, containerization with Docker, orchestration via Kubernetes, and automation through CI/CD pipelines. The methodology emphasizes observability, resilience, and developer productivity. Through a case implementation, the study evaluates system performance, scalability, and fault tolerance using tools such as Prometheus, Grafana, OpenTelemetry, and Resilience4j. The findings demonstrate that Java remains highly effective for building cloud-native systems, especially when paired with lightweight frameworks and modern DevOps practices. The research concludes with actionable recommendations for designing, deploying, and managing scalable microservices in modern enterprise environments.
Keywords
Java microservices, cloud-native architecture, Spring Boot, Quarkus, Kubernetes, Docker, CI/CD, observability, DevOps, resilience engineering, containerization, service orchestration, enterprise software.
Citations
IRE Journals:
Mohan Rao Pulugulla
"Engineering Cloud-Native Microservices in Java: A Scalable Approach for Modern Enterprise Software Architectures" Iconic Research And Engineering Journals Volume 9 Issue 3 2025 Page 1-10
IEEE:
Mohan Rao Pulugulla
"Engineering Cloud-Native Microservices in Java: A Scalable Approach for Modern Enterprise Software Architectures" Iconic Research And Engineering Journals, 9(3)
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