Optimizing Cloud-Native Micro service Architecture: Design Principles, Scalability, and Operational Resilience
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P116Keywords:
Cloud-Native Architecture, Microservices, Scalability, Devops, Kubernetes, Fault Tolerance, Distributed Systems, Resilience EngineeringAbstract
The rise of cloud-native microservice architecture has redefined how organizations design, deploy, and manage large-scale applications in distributed computing environments. This research examines the evolution, design principles, and optimization strategies underpinning cloud-native systems, emphasizing their scalability, resilience, and operational efficiency. By decomposing applications into independently deployable services, microservice architecture enables agility and continuous delivery, yet introduces new challenges in orchestration, observability, and security. Through a review of scholarly literature and industry case studies published up to 2022, this study explores the theoretical foundations of modular design, decentralized control, and adaptive scalability that define cloud-native systems. It further evaluates technologies such as Kubernetes, Docker, and service mesh frameworks that facilitate automated deployment and fault tolerance. Findings highlight that successful cloud-native adoption requires a balanced integration of automation, DevOps practices, and resilience engineering, supported by cultural and organizational transformation. The paper concludes by proposing a conceptual framework for sustainable optimization of microservices, identifying future directions involving AI-driven orchestration, edge computing, and AIOps-enabled self-healing systems. This study contributes to the growing body of knowledge on cloud-native computing by synthesizing empirical evidence and theoretical insights to guide future research and practical implementation.
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