ARCHIVES

The integration of artificial intelligence (AI) and machine learning (ML) into cloud computing has led to the emergence of distributed AI/ML applications operating across the cloud–edge continuum. However, privacy concerns, regulatory compliance, and ethical constraints present challenges in determining optimal workload distribution. This paper explores a strategic allocation framework that dynamically assigns AI/ML workloads between cloud and edge resources based on data sensitivity, computational efficiency, and policy requirements. By leveraging intelligent orchestration mechanisms, we propose an adaptive approach that enhances performance, ensures compliance with regulatory frameworks, and upholds ethical AI principles. Our findings contribute to the development of secure, efficient, and responsible AI/ML deployment in cloud-edge environments.