The Impact of Cloud-Native Architectures, Microservices, and Artificial Intelligence on Software Scalability
Received: 2026-05-29
Published: 2026-06-06
Abstract
Modern software engineering is characterized by a rapid transition from monolithic applications toward distributed cloud-native systems based on microservices architecture and artificial intelligence technologies. Increasing data volumes, growing user workloads, and the need for continuous digital service delivery require scalable, flexible, and resilient software systems. This study examines the impact of cloud computing, microservices architecture, and artificial intelligence on software scalability. The research is based on a comparative analysis of recent scientific publications and industry materials related to cloud-native architectures, DevOps practices, containerization, and intelligent automation. The results demonstrate that cloud-native architectures provide improved infrastructure flexibility and dynamic scalability, microservices increase fault tolerance and accelerate development processes, while artificial intelligence contributes to monitoring automation, resource optimization, and intelligent decision-making. However, integrating these technologies also introduces challenges related to architectural complexity, security, and distributed infrastructure management.
Keywords
List of references
-
Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing. National Institute of Standards and Technology.
-
Fowler, M., & Lewis, J. (2014). Microservices: A Definition of This New Architectural Term. Martin Fowler Blog.
-
Dragoni, N., Giallorenzo, S., Lafuente, A. L., Mazzara, M., Montesi, F., Mustafin, R., & Safina, L. (2017). Microservices: Yesterday, Today, and Tomorrow. In Present and Ulterior Software Engineering (pp. 195–216). Springer.
-
Burns, B., Grant, B., Oppenheimer, D., Brewer, E., & Wilkes, J. (2016). Borg, Omega, and Kubernetes. Communications of the ACM, 59(5), 50–57.
-
Reddy, K. S., & Sateesh, N. (2026). Software Applications in the Era of Cloud, Microservices, and Artificial Intelligence: A Study on Scalability and Developer Productivity.
-
Merkel, D. (2014). Docker: Lightweight Linux Containers for Consistent Development and Deployment. Linux Journal, 2014(239).
-
Sharma, et al. (2020). Cloud and Microservices Scalability Analysis.
-
Wang, et al. (2024). AI-based DevOps Automation and Developer Productivity.
-
Singh, et al. (2024). Cloud-native CI/CD Architectures for Distributed Applications.
-
Adams, et al. (2025). Scalability Evaluation in Distributed Cloud Systems.
About the Authors
License
This work is licensed under a Creative Commons Attribution 4.0 International License.