Cloud computing has become essential for modern digital services, yet efficiently allocating compute, storage, and network resources in large-scale and highly dynamic environments remains a significant challenge. Traditional rule-based approaches often struggle to cope with workload variability, multi-tenancy, and the need for real-time multi-objective optimization. In response, recent research has increasingly explored artificial intelligence techniques to improve prediction, scheduling, and automated resource control in cloud infrastructures. This study presents a comprehensive survey of AI-based methods for cloud resource allocation, including machine learning, deep learning, reinforcement learning, and hybrid approaches. It systematically analyzes selected studies published between 2020 and 2026, examining their learning paradigms, optimization objectives (e.g., performance, cost, energy efficiency), experimental validation strategies, and reported limitations. While classical optimization techniques are briefly discussed to contextualize the evolution of the field, the core analysis is strictly centered on AI-driven approaches. The study concludes by identifying the key challenges that persist in intelligent cloud resource management and outlines promising directions for future research toward more adaptive, reliable, and scalable optimization frameworks.
Doukha et al. (Thu,) studied this question.
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