Cloud Task Scheduling Based on Proximal Policy Optimization Algorithm for Lowering Energy Consumption of Data Center |
Yang, Yongquan
(Department of Computer Science and technology (Ocean University of China))
He, Cuihua (Department of Computer Science and technology (Ocean University of China)) Yin, Bo (Department of Computer Science and technology (Ocean University of China)) Wei, Zhiqiang (Department of Computer Science and technology (Ocean University of China)) Hong, Bowei (Department of Computer Science and technology (Ocean University of China)) |
1 | J. Yu, B. Zhang, Z. Kuang, D. Lin, and J. Fan, "iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning," IEEE Transactions on Information Forensics and Security, vol. 12, no. 5, pp. 1005-1016, May 2017. DOI |
2 | V. Mnih et al., "Playing Atari with Deep Reinforcement Learning," arXiv:1312.5602 [cs], Dec. 2013. |
3 | A. F. S. Devaraj, M. Elhoseny, S. Dhanasekaran, E. L. Lydia, and K. Shankar, "Hybridization of firefly and Improved Multi-Objective Particle Swarm Optimization algorithm for energy efficient load balancing in Cloud Computing environments," Journal of Parallel and Distributed Computing, vol. 142, pp. 36-45, Aug. 2020. DOI |
4 | D. Ding, X. Fan, Y. Zhao, K. Kang, Q. Yin, and J. Zeng, "Q-learning based dynamic task scheduling for energy-efficient cloud computing," Future Generation Computer Systems, vol. 108, pp. 361-371, Jul. 2020. DOI |
5 | W. McKinney, "Data Structures for Statistical Computing in Python," in Proc. of the 9th Python in Science Conference, pp. 56-61, 2010. |
6 | Martin Abadi et al., "TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems," 2015. [Online]. Available: https://www.tensorflow.org/ |
7 | Sukhpal Singh Gill et al., "AI for Next Generation Computing: Emerging Trends and Future Directions," Internet of Things, 2022. |
8 | Y. Yin, Y. Xu, W. Xu, M. Gao, L. Yu, and Y. Pei, "Collaborative Service Selection via Ensemble Learning in Mixed Mobile Network Environments," Entropy, vol. 19, no. 7, Jul. 2017. |
9 | J. D. Hunter, "Matplotlib: A 2D Graphics Environment," Computing in Science & Engineering, vol. 9, no. 03, pp. 90-95, May 2007. DOI |
10 | V. Mnih et al., "Human-level control through deep reinforcement learning," Nature, vol. 518, no. 7540, pp. 529-533, Feb. 2015. DOI |
11 | J. Stuart, Norvig, and Peter, Artificial Intelligence: A Modern Approach, 1995. |
12 | L. Y. Zuo and Z. B. Cao, "Review of scheduling research in cloud computing," Application Research of Computers, vol. 29, no. 11, pp. 4023-4027, 2012. DOI |
13 | C. J. C. H. Watkins, "Learning from delayed rewards," Ph.D. dissertation, King's College, Cambridge United Kingdom, 1989. |
14 | Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, May 2015. DOI |
15 | J. Yu, Z. Kuang, B. Zhang, W. Zhang, D. Lin, and J. Fan, "Leveraging Content Sensitiveness and User Trustworthiness to Recommend Fine-Grained Privacy Settings for Social Image Sharing," IEEE Transactions on Information Forensics and Security, vol. 13, no. 5, pp. 1317-1332, May 2018. DOI |
16 | S. Seth and N. Singh, "Dynamic heterogeneous shortest job first (DHSJF): a task scheduling approach for heterogeneous cloud computing systems," Int. j. inf. tecnol., vol. 11, no. 4, pp. 653-657, Dec. 2019. DOI |
17 | R. J. Williams, "Simple statistical gradient-following algorithms for connectionist reinforcement learning," Mach Learn, vol. 8, no. 3, pp. 229-256, May 1992. DOI |
18 | "Alibaba Cluster Trace Program," Alibaba, 2021. Accessed: Jan. 04, 2022. [Online]. Available: https://github.com/alibaba/clusterdata/blob/4221e02342dd01fd30a9800b19b7f365a3fd5ac8/cluster-trace-v2018/trace_2018.md |
19 | H. Peng, W.-S. Wen, M.-L. Tseng, and L.-L. Li, "Joint optimization method for task scheduling time and energy consumption in mobile cloud computing environment," Applied Soft Computing, vol. 80, pp. 534-545, Jul. 2019. DOI |
20 | J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz, "Trust Region Policy Optimization," in Proc. of the 32nd International Conference on Machine Learning, pp. 1889-1897, Jun. 2015. |
21 | "SimPy," Team SimPy, 2020. [Online]. Available: https://simpy.readthedocs.io/en/latest/index.html |
22 | C. He, Y. Yang, and B. Hong, "Cloud Task Scheduling Based on Policy Gradient Algorithm in Heterogeneous Cloud Data Center for Energy Consumption Optimization," in Proc. of 2020 International Conference on Internet of Things and Intelligent Applications (ITIA), pp. 1-5, Nov. 2020. |
23 | J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, "Proximal Policy Optimization Algorithms," arXiv:1707.06347 [cs], Aug. 2017. |
24 | C. Lu, K. Ye, G. Xu, C.-Z. Xu, and T. Bai, "Imbalance in the cloud: An analysis on Alibaba cluster trace," in Proc. of 2017 IEEE International Conference on Big Data (Big Data), pp. 2884-2892, Dec. 2017. |
25 | S. van der Walt, S. C. Colbert, and G. Varoquaux, "The NumPy Array: A Structure for Efficient Numerical Computation," Computing in Science Engineering, vol. 13, no. 2, pp. 22-30, Mar. 2011. |