Edge AI Architecture for Distributed Cloud Predictive Analytics and Real-Time Intelligent Enterprise Operations
DOI:
https://doi.org/10.21590/Keywords:
Edge AI, Distributed Cloud, Predictive Analytics, Enterprise Operations, Artificial Intelligence, Internet of Things, Machine Learning, Real-Time Analytics, Fog Computing, Intelligent SystemsAbstract
Edge Artificial Intelligence (Edge AI) has emerged as a transformative technology that enables intelligent data processing at or near the source of data generation, reducing latency and enhancing decision-making capabilities. The convergence of Edge AI with distributed cloud computing has created a robust architecture capable of supporting predictive analytics and real-time enterprise operations across industries. This study explores the architecture of Edge AI integrated with distributed cloud environments to facilitate intelligent enterprise operations through predictive data analytics. The proposed framework combines edge devices, fog computing, cloud infrastructure, artificial intelligence models, and Internet of Things (IoT) technologies to process large volumes of data efficiently while minimizing communication delays and bandwidth consumption. Distributed cloud platforms provide scalable storage and computational resources, whereas edge nodes execute real-time analytics for mission-critical applications. Predictive analytics models leverage machine learning algorithms to forecast operational events, equipment failures, customer behavior, and resource requirements. The architecture also addresses challenges related to security, privacy, interoperability, scalability, and resource optimization. The study demonstrates that integrating Edge AI with distributed cloud systems enhances operational efficiency, business agility, and intelligent automation. The findings contribute to the development of next-generation enterprise systems capable of delivering reliable, low-latency, and data-driven decision support in dynamic and highly connected business environments.
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