What question did this study set out to answer?

This survey aims to analyze machine learning-driven resource provisioning strategies in cloud computing.

Machine Learning-Driven Resource Provisioning in Modern Cloud Environments: A Taxonomic Survey

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Taxonomic survey categorizes ML strategies for resource management in cloud computing, highlighting implications.

This survey aims to analyze machine learning-driven resource provisioning strategies in cloud computing.
Comprehensive review of 35 key studies on ML resource provisioning from 2023 to 2025.
Taxonomic analysis focusing on ML methodologies and optimization objectives.
Quantitative synthesis demonstrating performance improvements over traditional methods.
Deep Reinforcement Learning models reduced resource consumption by 10% and improved performance by 30%.
Hybrid architectures achieved user cost reductions of up to 44%.
Shift from reactive heuristics to proactive, hybrid forecasting-optimization models is evident.

Kosim et al. (2026) studied this question.

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