AI-Driven Resource Optimization and Security in Cloud Computing Environments

Abstract

The rapid expansion of cloud computing and related technologies
has led to increasingly complex and resource-intensive environments,
necessitating advanced solutions for optimizing performance, security,
and cost-efficiency. This paper investigates various AI-driven approaches
to address challenges in cloud computing, focusing on resource
allocation, intrusion detection, and dynamic optimization of cloud
infrastructure. AI-based frameworks have shown potential in enhancing
security measures, improving energy efficiency, and managing resource
allocation dynamically. This work synthesizes recent advancements in
AI techniques, such as deep learning, reinforcement learning, and fuzzy
logic, to provide a comprehensive overview of how these technologies
are being leveraged in cloud computing. Key findings include the
efficacy of AI in mitigating distributed denial-of-service (DDoS) attacks
in fog computing, optimizing energy consumption in software-defined
networking (SDN) environments, and enhancing cybersecurity through
hybrid frameworks that combine cloud and on-device processing. The
study also highlights the role of predictive analytics and machine learning
algorithms in enhancing the reliability and performance of cloud services.
By analyzing existing literature, this paper aims to present a detailed
evaluation of current AI applications in cloud environments, outline
their benefits and limitations, and suggest future research directions.
Ultimately, this work serves as a resource for understanding how AI can
be harnessed to tackle some of the most pressing challenges in modern
cloud computing systems.