研究论文
Deep reinforcement learning for energy and time optimized scheduling of precedence-constrained tasks in edge–cloud computing environments
Amanda Jayanetti, Saman Kumara Halgamuge, Rajkumar Buyya
The University of Melbourne
来源Future Generation Computer Systems
年份2022
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学术脉络
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计算机 / AIIoT and Edge/Fog Computing
Cloud Computing and Resource Management · Blockchain Technology Applications and Security
参考文献 38
High-Dimensional Continuous Control Using Generalized Advantage Estimation
被引 1,700John Schulman, Philipp Moritz, Sergey Levine · arXiv (Cornell University) · 2015
PASTA: a power-aware solution to scheduling of precedence-constrained tasks on heterogeneous computing resources
被引 26Mohsen Sharifi, Saeed Shahrivari, Hadi Salimi · Computing · 2012
Simple statistical gradient-following algorithms for connectionist reinforcement learning
被引 7,377Ronald J. Williams · Machine Learning · 1992
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引用本文 129
Multi-Agent Deep Reinforcement Learning Framework for Renewable Energy-Aware Workflow Scheduling on Distributed Cloud Data Centers
被引 117Amanda Jayanetti, Saman Kumara Halgamuge, Rajkumar Buyya · IEEE Transactions on Parallel and Distributed Systems · 2024
A decade of research in fog computing: Relevance, challenges, and future directions
被引 103Satish Narayana Srirama · Software Practice and Experience · 2023
An energy-efficient and deadline-aware workflow scheduling algorithm in the fog and cloud environment
被引 87Navid Khaledian, Keyhan Khamforoosh, Reza Akraminejad · Computing · 2023
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