A scheduling scheme in the cloud computing environment using deep Q-learning
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Zhao Tong | Keqin Li | Kenli Li | Hongjian Chen | Xiaomei Deng | Xiaomei Deng | Keqin Li | KenLi Li | Zhao Tong | Hongjian Chen
[1] Ümit V. Çatalyürek,et al. Compaction of Schedules and a Two-Stage Approach for Duplication-Based DAG Scheduling , 2009, IEEE Transactions on Parallel and Distributed Systems.
[2] Shijun Liu,et al. DRL-Scheduling: An Intelligent QoS-Aware Job Scheduling Framework for Applications in Clouds , 2018, IEEE Access.
[3] Li Wang,et al. Hierarchical Deep Multiagent Reinforcement Learning , 2018, ArXiv.
[4] Hamid Arabnejad,et al. List Scheduling Algorithm for Heterogeneous Systems by an Optimistic Cost Table , 2014, IEEE Transactions on Parallel and Distributed Systems.
[5] Sridhar Mahadevan,et al. Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes , 2007, J. Mach. Learn. Res..
[6] Junzhou Luo,et al. Data Placement and Task Scheduling Optimization for Data Intensive Scientific Workflow in Multiple Data Centers Environment , 2014 .
[7] Zhaohong Wang,et al. Dynamic Engineering Multi-criteria Decision Making Model Optimized by Entropy Weight for Evaluating Bid , 2012 .
[8] Rajkumar Buyya,et al. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..
[9] Miron Livny,et al. Pegasus, a workflow management system for science automation , 2015, Future Gener. Comput. Syst..
[10] Kenli Li,et al. Scheduling Precedence Constrained Stochastic Tasks on Heterogeneous Cluster Systems , 2015, IEEE Transactions on Computers.
[11] Ewa Deelman,et al. WorkflowSim: A toolkit for simulating scientific workflows in distributed environments , 2012, 2012 IEEE 8th International Conference on E-Science.
[12] Goran Strbac,et al. Recurrent Deep Multiagent Q-Learning for Autonomous Agents in Future Smart Grid , 2018, AAMAS.
[13] Luan Teylo,et al. A hybrid evolutionary algorithm for task scheduling and data assignment of data-intensive scientific workflows on clouds , 2017, Future Gener. Comput. Syst..
[14] Qingsheng Zhu,et al. Deadline-Constrained Cost Optimization Approaches for Workflow Scheduling in Clouds , 2017, IEEE Transactions on Parallel and Distributed Systems.
[15] S. Haykin,et al. A Q-learning-based dynamic channel assignment technique for mobile communication systems , 1999 .
[16] Dharma P. Agrawal,et al. Improving scheduling of tasks in a heterogeneous environment , 2004, IEEE Transactions on Parallel and Distributed Systems.
[17] Salim Hariri,et al. Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing , 2002, IEEE Trans. Parallel Distributed Syst..
[18] Li Fu,et al. Time and Energy Optimization Algorithms for the Static Scheduling of Multiple Workflows in Heterogeneous Computing System , 2017, Journal of Grid Computing.
[19] Xin Shen,et al. Real-time workflows oriented online scheduling in uncertain cloud environment , 2017, The Journal of Supercomputing.
[20] Mei-Hui Su,et al. Characterization of scientific workflows , 2008, 2008 Third Workshop on Workflows in Support of Large-Scale Science.
[21] Hong Liu,et al. QL-HEFT: a novel machine learning scheduling scheme base on cloud computing environment , 2019, Neural Computing and Applications.
[22] Ji Li,et al. DRL-cloud: Deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers , 2018, 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC).
[23] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[24] Witold Pedrycz,et al. A Clustering-Based Graph Laplacian Framework for Value Function Approximation in Reinforcement Learning , 2014, IEEE Transactions on Cybernetics.
[25] Jie Xu,et al. Data-driven dynamic resource scheduling for network slicing: A Deep reinforcement learning approach , 2019, Inf. Sci..
[26] Xiaoyong Tang,et al. Budget‐constraint stochastic task scheduling on heterogeneous cloud systems , 2017, Concurr. Comput. Pract. Exp..
[27] Seung Ho Hong,et al. A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach , 2018, Applied Energy.
[28] Tao Yang,et al. A Comparison of Clustering Heuristics for Scheduling Directed Acycle Graphs on Multiprocessors , 1992, J. Parallel Distributed Comput..
[29] Keqin Li,et al. Heterogeneity-driven end-to-end synchronized scheduling for precedence constrained tasks and messages on networked embedded systems , 2015, J. Parallel Distributed Comput..
[30] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[31] Jian Ma,et al. Learning-Based Energy-Efficient Data Collection by Unmanned Vehicles in Smart Cities , 2018, IEEE Transactions on Industrial Informatics.
[32] Weiwei Lin,et al. A list scheduling algorithm for heterogeneous systems based on a critical node cost table and pessimistic cost table , 2017, Concurr. Comput. Pract. Exp..
[33] Ann L. Chervenak,et al. Characterizing and profiling scientific workflows , 2013, Future Gener. Comput. Syst..
[34] Ewa Deelman,et al. Community Resources for Enabling Research in Distributed Scientific Workflows , 2014, 2014 IEEE 10th International Conference on e-Science.
[35] Jianye Hao,et al. Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces , 2019, IJCAI.
[36] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[37] Rizos Sakellariou,et al. Scheduling multiple DAGs onto heterogeneous systems , 2006, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.
[38] Laurence T. Yang,et al. Energy-Efficient Scheduling for Real-Time Systems Based on Deep Q-Learning Model , 2019, IEEE Transactions on Sustainable Computing.
[39] Gavin Taylor,et al. Kernelized value function approximation for reinforcement learning , 2009, ICML '09.
[40] Aihua Liang,et al. A Novel, Energy-Aware Task Duplication-Based Scheduling Algorithm of Parallel Tasks on Clusters , 2016 .
[41] Kenli Li,et al. A novel task scheduling scheme in a cloud computing environment using hybrid biogeography-based optimization , 2019, Soft Comput..
[42] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[43] Radu Prodan,et al. Multi-objective workflow scheduling in Amazon EC2 , 2014, Cluster Computing.
[44] Martin L. Puterman,et al. Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .
[45] Hidenori Nakazato,et al. Prior node selection for scheduling workflows in a heterogeneous system , 2017, J. Parallel Distributed Comput..
[46] Hamid Arabnejad,et al. Multi-QoS constrained and Profit-aware scheduling approach for concurrent workflows on heterogeneous systems , 2017, Future Gener. Comput. Syst..
[47] Eric R. Ziecel. Four Days With Dr. Deming , 1995 .
[48] Goran Strbac,et al. Recurrent Deep Multiagent Q-Learning for Autonomous Brokers in Smart Grid , 2018, IJCAI.
[49] Kenli Li,et al. Maximizing reliability with energy conservation for parallel task scheduling in a heterogeneous cluster , 2015, Inf. Sci..