Containerized Artificial Intelligent System Design in Cloud and Cyber-Physical Systems

Abstract

The integration of Artificial Intelligence (AI) into cloud computing and Cyber-Physical Systems (CPS) is crucial for achieving efficiency, scalability, and real-time capabilities in modern ecosystems. Containerization enhances AI deployment by improving portability, resource efficiency, and system isolation. This article addresses key design considerations and challenges in implementing containerized AI within cloud-native and CPS environments, focusing on scalability, fault tolerance, real-time responsiveness, and security. Through research analysis and case studies, it explores strategies for optimizing AI workload distribution across cloud and edge infrastructure to meet CPS demands. Future directions, including hybrid architectures and federated learning, are also discussed to support scalable, secure, and reliable AI systems for next-generation cloud and CPS applications.