AI-Driven Microservice Architectures: Enhancing Compliance and Decision Intelligence in Cloud Environments
Main Article Content
Abstract
The application of AI-based microservice architecture (MSAs) on cloud is a revolution that has changed how organizations react to compliance and decision intelligence. The current research paper discusses the concept of integrating AI and microservice-based architectures to develop the decision-making processes further and provide more compliance in dynamic clouds. As companies shift to the cloud at a rather fast pace, it has become an extremely significant concern to ascertain that the necessary regulations are achieved without impacting the proper operation. These requirements cannot be satisfied by the conventional monolithic architectures but can be scaled, flexible and adaptable to the regulatory requirements. All these architectures are automatable in order to make decisions through the application of AI to predict compliance risks and continuously monitor the system behaviors in order to be compliant with the ever-changing regulations.
The paper contains a schematic implementation of the AI-driven microservices including the aspects of real-time compliance control, automated decision support, and dynamic implementation of policies. AI-based analytics, rule-based engines and distributed system management are considered to be the most significant elements of the structure. The paper also analyses the impact of such architecture in enhancing data processing, efficiency of operations and provision of competitive advantage of predictive capabilities.
The demonstrative examples of how artificial intelligence controlled MSAs are applied are real-life case studies and simulations that are likely to expose compliance gaps, keep the data confidential, and optimise the utilisation of cloud resources. The findings point to the disruptive aspect of AI in a microservice systems, particularly in cloud systems where compliance and decision intelligence are most prominent. Under this framework, organizations have been able to achieve greater agility, enhanced compliance and make smarter decisions.
Article Details
Section
How to Cite
References
1. N. Dragoni, et al., "Microservices: Yesterday, Today, and Tomorrow," in Present and Ulterior Software Engineering, Springer, Cham, 2017.
2. D. Merkel, "Docker: lightweight linux containers for consistent development and deployment," Linux Journal, vol. 2014, no. 239, 2014.
3. J. Liu, et al., "Artificial intelligence in the 21st century," IEEE Access, vol. 6, 2018.
4. Z. Kotti, R. Galanopoulou, and D. Spinellis, "Machine learning for software engineering: A tertiary study," ACM Comput. Surv., vol. 55, no. 12, 2023.
5. K. Petersen, et al., "Guidelines for conducting systematic mapping studies in software engineering: An update," Information and Software Technology, vol. 64, 2015.
6. AI Watch, "Defining Artificial Intelligence 2.0," Publications Office of the European Union, 2021. [Online]. Available: https://doi.org/10.2760/019901.
7. International Organization For Standardization, ISO/IEC 25010 - Systems and Software Engineering - Systems and Software Quality Requirements and Evaluation (SQuaRE) - System and Software Quality Models vol. 2, 2011.
8. T. Erl, Service-Oriented Architecture: Concepts, Technology, and Design, Prentice Hall PTR, Upper Saddle River, NJ, USA, 2005.
9. M. Fowler and J. Lewis, "Microservices," 2014. [Online]. Available: https://shorturl.at/7zOPl. Accessed: Mar. 16, 2023.
10. S. Moreschini, et al., "MLOps for evolvable AI intensive software systems," in SANER 2022, 2022.