AI APPLICATIONS IN CLOUD COMPUTING TECHNOLOGIES

Artificial intelligence (AI) is fundamentally reshaping cloud computing — transforming data centres, infrastructure management, security operations, and software development
pipelines from reactive, rule-based systems into intelligent, self-optimising platforms. This
paper presents a comprehensive review of AI methodologies applied across cloud computing domains, including deep reinforcement learning (RL) for resource scheduling and energy management, supervised machine learning for predictive auto-scaling and cost optimisation,
anomaly detection for cloud security, large language models (LLMs) for AIOps and DevOps automation, federated learning for privacy-preserving multi-cloud analytics, and generative
AI for infrastructure-as-code generation. We examine landmark industry deployments of 2020–2024 — including Google DeepMind's 40% reduction in data-centre cooling energy, Microsoft Azure's GNN-based incident management system, and Meta's reinforcement learning compiler optimisation — and provide structured comparisons of traditional and AIenhanced cloud operations. Ethical challenges specific to AI in cloud environments are
examined, including multi-tenant data privacy, autonomous decision risk, algorithmic bias in resource allocation, and the environmental cost of AI inference at scale. The paper concludes with a forward-looking discussion on AI-native cloud architectures, autonomous cloud operations, and the governance frameworks required for responsible AI deployment in critical cloud infrastructure.