Integrating Two-Level Reinforcement Learning Process for Enhancing Task Scheduling Efficiency in a Complex Problem-Solving Environment

References

• A. Asghari, M. K. Sohrabi, and F. Yaghmaee, “Task scheduling, resource provisioning, and load balancing on scientific workflows using parallel SARSA reinforcement learning agents and genetic algorithm,” J. Supercomput., Vol. 77, no. 3, pp. 2800–28, 2020. DOI: 10.1007/s11227-020-03364-1.
   Google Scholar
• P. Nawrocki, and B. Sniezynski, “Adaptive context-aware energy optimization for services on mobile devices with use of machine learning,” Wirel. Pers Commun., Vol. 115, no. 3, pp. 1839–67, 2020. DOI: 10.1007/s11277-020-07657-9.
   Google Scholar
• X. Sui, D. Liu, L. Li, H. Wang, and H. Yang, “Virtual machine scheduling strategy based on machine learning algorithms for load balancing,” J. Wirel. Commun. Netw., Vol. 2019, pp. 160, 2019. DOI: 10.1186/s13638-019-1454-9.
   Google Scholar
• S. Afzal, and G. Kavitha, “Load balancing in cloud computing – a hierarchical taxonomical classification,” J. Cloud Comput., Vol. 8, no. 22, pp. 1–24, 2019. DOI: 10.1186/s13677-019-0146-7.
   Google Scholar
• Y. Zhang, X. Cheng, L. Chen, and H. Shen, “Energy-efficient tasks scheduling heuristics with multi-constraints in virtualized clouds,” J. Grid Comput.., Vol. 16, pp. 459–75, 2018. DOI: 10.1007/s10723-018-9426-6.
   Google Scholar
• Y. Khosiawan, Y. Park, I. Moon, J. M. Nilakantan, and I. Nielsen, “Task scheduling system for UAV operations in indoor environment,” Neural Comput & Applic., Vol. 31, pp. 5431–59, 2019. DOI: 10.1007/s00521-018-3373-9.
   Google Scholar
• J. Ge, B. Liu, T. Wang, Q. Yang, A. Liu, and A. Li, “Q-learning based flexible task scheduling in a global view for the internet of things,” Trans, Emerg. Tel. Tech., Vol. 32, pp. 1–32, 2020. DOI: 10.1002/ett.4111.
   Google Scholar
• M. Melnik, and D. Nasonov, “Workflow scheduling using neural networks and reinforcement learning,” Procedia Comput. Sci., Vol. 156, pp. 29–36, 2019. DOI: 10.1016/j.procs.2019.08.126.
   Google Scholar
• B. Waschneck, A. Reichstaller, L. Belzner, T. Altenmüller, T. Bauernhansl, A. Knapp, and A. Kyek, “Optimization of global production scheduling with deep reinforcement learning,” Procedia CIRP, Vol. 72, pp. 1264–69, 2018. DOI: 10.1016/j.procir.2018.03.212.
   Google Scholar
• D. Kim, T. Lee, S. Kim, B. Lee, and H. Y. Youn, “Adaptive packet scheduling in IoT environment based on Q-learning,” J. Ambient Intell. Human Comput., Vol. 11, pp. 2225–35, 2020. DOI: 10.1007/s12652-019-01351-w.
   Google Scholar
• P. Zhang, X. Ma, Y. Xiao, W. Li, and C. Lin, “Two-level task scheduling with multi-objectives in geo-distributed and large-scale SaaS cloud,” World Wide Web, Vol. 22, pp. 2291–319, 2019. DOI: 10.1007/s11280-019-00680-2.
   Google Scholar
• F. Benda, R. Braune, K. F. Doerner, and R. F. Hartl, “A machine learning approach for flow shop scheduling problems with alternative resources, sequence-dependent setup times, and blocking,” OR Spectr., Vol. 41, pp. 871–93, 2019. DOI: 10.1007/s00291-019-00567-8.
   Web of Science ®Google Scholar
• K. Karthiban, and J. S. Raj, “An efficient green computing fair resource allocation in cloud computing using modified deep reinforcement learning algorithm,” Soft. comput., Vol. 24, pp. 14933–42, 2020. DOI: 10.1007/s00500-020-04846-3.
   Google Scholar
• Z. Tang, W. Jia, X. Zhou, W. Yang, and Y. You, “Representation and reinforcement learning for task scheduling in edge computing,” IEEE Transactions on Big Data, Vol. 8, no. 3, pp. 795–808, 2020. DOI: 10.1109/TBDATA.2020.2990558.
   Google Scholar
• H. Meng, D. Chao, Q. Guo, and X. Li, “Delay-sensitive task scheduling with deep reinforcement in mobile-edge computing systems,” J. Phys.: Conf. Ser., Vol. 1229, pp. 012059, 2019. DOI: 10.1088/17426596/1229/1/012059.
   Google Scholar
• J. Wang, J. Cao, S. Wang, Z. Yao, and W. Li, “IRDA: incremental reinforcement learning for dynamic resource allocation,” IEEE Trans. Big Data, Vol. 8, no. 3, pp. 770–83, 2020. DOI: 10.1109/TBDATA.2020.2988273.
   Google Scholar
• M. M. Amiri, and D. Gündüz, “Computation scheduling for distributed machine learning with straggling workers,” in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 2019, pp. 8177–81, DOI: 10.1109/ICASSP.2019.8682911.
   Google Scholar
• M. Gaafar, M. Shaghaghi, R. S. Adve, and Z. Ding, “Reinforcement learning for cognitive radar task scheduling,” in 2019 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2019, pp. 1653–57, DOI: 10.1109/IEEECONF44664.2019.9048892.
   Google Scholar
• M. Soualhia, F. Khomh, and S. Tahar, “A dynamic and failure-aware task scheduling framework for hadoop,” IEEE Trans. Cloud Comput., Vol. 8, no. 2, pp. 553–69, 1 Apr—Jun. 2020. DOI: 10.1109/TCC.2018.2805812.
   Web of Science ®Google Scholar
• X. Wang, Z. Ning, S. Guo, and L. Wang, “Imitation learning enabled task scheduling for online vehicular edge computing,” IEEE Trans. Mobile Comput., Vol. 21, no. 2, pp. 598–611, 2020. DOI: 10.1109/TMC.2020.3012509.
   Google Scholar
• Z. Hu, B. Li, and J. Luo, “Time- and cost- efficient task scheduling across geo-distributed data centers,” IEEE Trans. Parallel Distrib. Syst., Vol. 29, no. 3, pp. 705–18, 1 Mar. 2018. DOI: 10.1109/TPDS.2017.2773504.
   Google Scholar
• G. L. Stavrinides, and H. D. Karatza, “Scheduling real-time bag-of-tasks applications with approximate computations in SaaS clouds,” Concurr. Computat. Pract. Exp., Vol. 32, no. 1, pp. e4208, 2017. DOI: 10.1002/cpe.4208.
   Google Scholar
• Z. Zang, W. Wang, Y. Song, L. Lu, W. Li, Y. Wang, and Y. Zhao, “Hybrid deep neural network scheduler for job-shop problem based on convolution two-dimensional transformation,” Comput. Intell. Neurosci., Vol. 2019, pp. 7172842, 2019. DOI: 10.1155/2019/7172842.
   PubMed Web of Science ®Google Scholar
• P. Zhang, and M. Zhou, “Dynamic cloud task scheduling based on a two-stage strategy,” IEEE Trans. Autom. Sci. Eng., Vol. 15, no. 2, pp. 772–83, Apr. 2018. DOI: 10.1109/TASE.2017.2693688.
   Web of Science ®Google Scholar
• X. Zheng, M. Li, and J. Guo, “” task scheduling using edge computing system in smart city”,” Int. J. Commun. Syst., Vol. 34, no. 6, pp. e4422, 2020. DOI: 10.1002/dac.4422.
   Google Scholar
• R. Shenbaga Moorthy, and P. Pabitha, “Optimal provisioning and scheduling of analytics as a service in cloud computing,” Trans. Emerg. Tel. Tech., Vol. 30, pp. e3609, 2019. DOI: 10.1002/ett.3609.
   Google Scholar
• F. Ebadifard, and S. M. Babamir, “A PSO-based task scheduling algorithm improved using a load-balancing technique for the cloud computing environment,” Concurr. Comput. Pract. Exp., Vol. 30, pp. e4368, 2017. DOI: 10.1002/cpe.4368.
   Google Scholar
• R. Khorsand, and M. Ramezanpour, “An energy efficient task scheduling algorithm based on a multicriteria decision-making method in cloud computing,” Int. J. Commun. Syst., Vol. 33, pp. e4379, 2020. DOI: 10.1002/dac.4379.
   Web of Science ®Google Scholar
• T. Dong, F. Xue, C. Xiao, and J. Li, “Task scheduling based on deep reinforcement learning in a cloud manufacturing environment,” Concurr. Comput. Pract. Exp., Vol. 32, pp. e5654, 2020. DOI: 10.1002/cpe.5654.
   Web of Science ®Google Scholar
• K. R. Prasanna Kumar, and K. Kittusamy, “Amelioration of task scheduling in cloud computing using crow search algorithm,” Neural Comput. Appl., Vol. 32, pp. 5901–7, 2020. DOI: 10.1007/s00521-019-04067-2.
   Google Scholar
• D. S. Rani, and M. Pounambal, “Deep learning based dynamic task offloading in mobile cloudlet environments,” Evol. Intel., Vol. 14, no. 2, pp. 499–507, 2019. DOI: 10.1007/s12065-019-00284-9.
   Google Scholar
• Z. Zhou, F. Li, H. Zhu, H. Xie, J. H. Abawajy, and M. U. Chowdhury, “An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments,” Neural Comput. Applic., Vol. 32, pp. 1531–41, 2020. DOI: 10.1007/s00521-019-04119-7.
   Web of Science ®Google Scholar
• Z. Peng, J. Lin, D. Cui, Q. Li, and J. He, “A multi-objective trade-off framework for cloud resource scheduling based on the deep Q-network algorithm,” Cluster. Comput., Vol. 32, pp. 1531–1541, 2020. DOI: 10.1007/s10586-019-03042-9.
   Google Scholar