AI-Driven Strategies for Optimization, Security, and Resource Management in Cloud and Fog Computing: A Comprehensive Analysis of Modern Machine Learning Applications

Abstract

The increasing adoption of cloud and fog computing has transformed the
landscape of distributed systems, enabling scalable and flexible solutions
for various applications. However, these technologies face significant
challenges related to security, resource management, and energy
efficiency. This paper provides a comprehensive analysis of AI-driven
approaches for optimizing cloud and fog computing environments,
focusing on intrusion detection, dynamic resource allocation, and energy
optimization. Key methods such as machine learning, deep learning,
and reinforcement learning are examined for their roles in enhancing
the performance and security of cloud infrastructures. Techniques like
AI-based intrusion detection systems (IDS) and hybrid deep learning
frameworks are discussed as critical components for mitigating cyber
threats in decentralized systems. Furthermore, AI’s impact on energy
optimization and resource allocation in Software-Defined Networking
(SDN)-based cloud infrastructures is highlighted, emphasizing the trade-
offs between cost, energy efficiency, and network performance. This study
synthesizes recent advancements and proposes future directions for
integrating AI into cloud and fog computing, aiming to address existing
vulnerabilities and improve overall system efficiency. A combination of
theoretical insights and practical implementations illustrates the potential
of AI technologies to revolutionize cloud computing management.
The findings underscore the critical need for continued research and
development of AI models tailored to the dynamic and complex nature
of modern computing environments. Key contributions are drawn
from multiple studies, providing a foundation for understanding the
convergence of AI and cloud computing technologies in advancing next-
generation computing systems.