Mutated Deep Reinforcement Learning Scheduling in Cloud for Resource-Intensive IoT Systems
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-023-10709-5.pdf
Reference19 articles.
1. Rjoub, G., Bentahar, J., Abdel Wahab, O., & Saleh Bataineh, A. (2021). Deep and reinforcement learning for automated task scheduling in large-scale cloud computing systems. Concurrency and Computation: Practice and Experience, 33(23), e5919.
2. Pal, S., Jhanjhi, N. Z., Abdulbaqi, A. S., Akila, D., Alsubaei, F. S., & Almazroi, A. A. (2023). An intelligent task scheduling model for hybrid internet of things and cloud environment for big data applications. Sustainability, 15(6), 5104.
3. Chen, W., Qiu, X., Cai, T., Dai, H. N., Zheng, Z., & Zhang, Y. (2021). Deep reinforcement learning for Internet of Things: A comprehensive survey. IEEE Communications Surveys & Tutorials, 23(3), 1659–1692.
4. Onwubolu, G. C., & Babu, B. V. (2013). New optimization techniques in engineering (Vol. 141). Springer.
5. Zhu, J., Song, Y., Jiang, D., & Song, H. (2017). A new deep-Q-learning-based transmission scheduling mechanism for the cognitive Internet of Things. IEEE Internet of Things Journal, 5(4), 2375–2385.
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