American Journal of Advanced Multidisciplinary Research and Innovation

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Cloud-Native Computing Architectures for Scalable Enterprise Solutions

Author(s) Barbara Engelhardt
Country United States
Abstract Cloud-native computing has emerged as a transformative paradigm for designing, developing, deploying, and managing modern enterprise applications. Unlike traditional monolithic architectures, cloud-native systems leverage microservices, containerisation, Kubernetes orchestration, serverless computing, DevSecOps, Continuous Integration/Continuous Deployment (CI/CD), Artificial Intelligence (AI), Machine Learning (ML), Edge Computing, Internet of Things (IoT), Service Mesh, Infrastructure as Code (IaC), Observability Platforms, Cloud Security, Zero Trust Architecture, and Multi-Cloud/Hybrid Cloud environments to provide scalable, resilient, secure, and highly available enterprise solutions. These technologies enable organisations to accelerate digital transformation, improve operational efficiency, reduce infrastructure costs, and rapidly deliver innovative digital services.
This study presents a comprehensive analysis of Cloud-Native Computing Architectures for Scalable Enterprise Solutions. A qualitative analytical research methodology based on secondary data is adopted to examine cloud-native architectural principles, enterprise deployment strategies, enabling technologies, implementation challenges, and future innovations. The research investigates how cloud-native architectures improve scalability, elasticity, fault tolerance, security, application portability, business agility, and intelligent automation across healthcare, finance, manufacturing, education, retail, government, and smart city ecosystems.
The findings indicate that cloud-native computing significantly enhances enterprise performance through container-based application deployment, automated orchestration, infrastructure elasticity, intelligent workload management, and continuous software delivery. Kubernetes enables efficient orchestration of distributed applications, while DevSecOps integrates security throughout the software development lifecycle. AI-powered cloud operations (AIOps) automate monitoring, anomaly detection, predictive maintenance, and resource optimisation. Serverless computing further reduces operational complexity by dynamically allocating computing resources according to application demand.
Despite these opportunities, challenges remain concerning cloud security, data privacy, vendor lock-in, interoperability, cost optimisation, distributed system complexity, regulatory compliance, and skills shortages. Future research should investigate AI-native cloud platforms, quantum cloud computing, autonomous cloud management, sustainable green cloud infrastructures, edge–cloud integration, and secure multi-cloud orchestration frameworks.
The study concludes that cloud-native computing architectures provide the technological foundation for scalable, resilient, and intelligent enterprise solutions by integrating modern software engineering practices, cloud technologies, cybersecurity, and intelligent automation to support sustainable digital transformation.
Keywords Cloud-Native Computing, Microservices, Kubernetes, Containerisation, Cloud Architecture, Enterprise Computing, DevSecOps, Serverless Computing, Multi-Cloud, Digital Transformation.
Field Engineering
Published In Volume 2, Issue 6, November-December 2020
Published On 2020-11-03
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