Department of EEE, Daffodil International University, Daffodil Smart City (DSC), Dhaka-1216, Dhaka, Bangladesh.
Review Article
World Journal of Advanced Research and Reviews, 2022, 16(03), 1230-1238
Received on 11 November 2022; revised on 20 December 2022; accepted on 23 December 2022
This paper explores the challenges associated with cloud resource management, the application of ML techniques to address these challenges, and their associated benefits and limitations. Key ML applications in cloud computing include workload prediction, energy-efficient VM consolidation, QoS-aware resource provisioning, and network-aware VM placement. The study also identifies research gaps and proposes future directions for enhancing ML-driven resource management in cloud environments, with a focus on deep learning, reinforcement learning, and ensemble methods. By leveraging ML, cloud computing systems can achieve improved scalability, cost-effectiveness, and performance, paving the way for next-generation intelligent cloud infrastructure.
Cloud Computing; Machine Learning; Resource Management; Workload Prediction; VM Consolidation; Energy Efficiency; Deep Learning; Reinforcement Learning; QoS Optimization.
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Tanvir Mahmud. ML-driven resource management in cloud computing. World Journal of Advanced Research and Reviews, 2022, 16(3), 1230-1238. Article DOI: https://doi.org/10.30574/wjarr.2022.16.3.1398