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Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions [article]

Guangyao Zhou, Wenhong Tian, Rajkumar Buyya
2021 arXiv   pre-print
Deep reinforcement learning (DRL), a combination of deep learning (DL) and reinforcement learning (RL), is one branch of the machine learning and has a considerable prospect in resource scheduling of Cloud  ...  Machine learning, a utility method to tackle problems in complex scenes, is used to resolve the resource scheduling of Cloud computing as an innovative idea in recent years.  ...  A novel type of machine learning policy for resource scheduling in Cloud computing is the combination of deep neural network (DNN) and reinforcement learning (RL), called deep reinforcement learning (DRL  ... 
arXiv:2105.04086v1 fatcat:rdfwf2ltffcyxhmohf6kwtqaai

2021 Index IEEE Transactions on Parallel and Distributed Systems Vol. 32

2022 IEEE Transactions on Parallel and Distributed Systems  
Li, M., +, TPDS July 2021 1842-1853 ADRL: A Hybrid Anomaly-Aware Deep Reinforcement Learning-Based Resource Scaling in Clouds.  ...  Li, X., +, TPDS July 2021 1690- 1701 The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs With Hybrid Parallelism.  ...  Graph coloring Feluca: A Two-Stage Graph Coloring Algorithm With Color-Centric Paradigm on GPU. Zheng, Z., +,  ... 
doi:10.1109/tpds.2021.3107121 fatcat:e7bh2xssazdrjcpgn64mqh4hb4