Dynamic resource provisioning is a critical challenge in cloud computing, offering the necessary elasticity to guarantee reliable services within a usage-based payment framework. With the evolution of distributed systems, traditional threshold-based provisioning methods are increasingly inadequate for managing highly dynamic workloads. This inadequacy necessitates adaptive, machine learning (ML)-driven approaches capable of forecasting demand and autonomously optimizing scheduling. This survey presents a comprehensive review of recent ML-based resource provisioning strategies in cloud computing. Through a rigorous taxonomic analysis of 35 key studies, with a focus on developments from 2023 to 2025, the research categorizes existing work along two primary dimensions: ML methodology, including classical, deep learning, and advanced reinforcement learning, and optimization objectives, such as cost, Quality of Service (QoS), sustainability, and security-aware paradigms. The findings reveal a paradigm shift from reactive heuristics to proactive, hybrid forecasting-optimization models, Multi-Agent Reinforcement Learning (MARL), and serverless computing orchestration. Quantitative synthesis demonstrates that intelligence-driven interventions offer measurable improvements over traditional methods. For example, Deep Reinforcement Learning (DRL) models have reduced resource consumption by 10% and improved performance by 30%, while hybrid architectures have achieved user cost reductions of up to 44%. The survey concludes by discussing fundamental tradeoffs and identifying critical open challenges and future research directions in the edge-cloud continuum, including predictive container pre-warming and carbon-aware green AI orchestration.
Kosim et al. (Thu,) studied this question.