Cloud-Native Micro services Architecture

Authors

  • Sri Harsha Koneru Computer Information Systems and Information Technology, University of Central Missouri, USA. Author
  • Ravi Teja Avireneni Industrial Management, University of Central Missouri, USA. Author
  • Naresh Kiran Kumar Reddy Yelkoti Information Systems Technology and Information Assurance, Wilmington University, USA. Author
  • Siva Prasad Yerneni Khaga Environmental Engineering, University of New Haven, USA. Author

DOI:

https://doi.org/10.63282/3050-9246.IJETCSIT-V2I4P110

Keywords:

Cloud-Native Architecture, Micro services, Containerization, Kubernetes, Docker, AI Workloads, MLOps, Scalability, Resilience, CI/CD, Distributed Systems, Service Orchestration

Abstract

The emergence of cloud-native microservices architecture has redefined how modern AI-driven systems are developed, deployed, and maintained. Unlike traditional monolithic systems, microservices enable modular development, independent scaling, and resilience across distributed environments (Dragoni et al., 2017). With the increasing demand for AI applications, integrating machine learning workloads into containerized and orchestrated microservices environments offers enhanced flexibility and scalability (Kalske, Mäkitalo, & Mikkonen, 2018). Cloud-native ecosystems leverage technologies such as Docker and Kubernetes to facilitate continuous integration and deployment (CI/CD), automated scaling, and fault isolation (Pahl & Jamshidi, 2016). Furthermore, adopting microservices in AI pipelines improves system observability and accelerates model iteration cycles through MLOps practices (Karmakar & Saha, 2020). However, challenges remain in managing data consistency, inter-service communication, and runtime monitoring at scale (Fazio et al., 2020). This research investigates the principles, advantages, and architectural considerations of deploying AI workloads in cloud-native microservices environments to enhance scalability, resilience, and operational efficiency. The findings contribute to understanding how enterprises can leverage microservices and container orchestration for intelligent, adaptive, and high-performing AI systems

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References

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Published

2021-12-30

Issue

Vol. 2 No. 4 (2021)

Section

Articles

How to Cite

1.
Koneru SH, Avireneni RT, Reddy Yelkoti NKK, Yerneni Khaga SP. Cloud-Native Micro services Architecture. IJETCSIT [Internet]. 2021 Dec. 30 [cited 2025 Dec. 12];2(4):86-94. Available from: https://www.ijetcsit.org/index.php/ijetcsit/article/view/492

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