Harnessing reinforcement learning in fog-cloud computing: challenges, insights, and future directions
The fast-changing world of fog-cloud computing poses various challenges and opportunities, especially in terms of optimizing resources, adaptability, and system efficiency. Reinforcement Learning (RL) is a powerful tool to tackle these challenges due to its ability to learn and adjust from interacti...
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Little Lion Scientific R&D
2024
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Online Access: | http://psasir.upm.edu.my/id/eprint/111000/1/20Vol102No5.pdf http://psasir.upm.edu.my/id/eprint/111000/ http://www.jatit.org/volumes/Vol102No5/20Vol102No5.pdf |
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my.upm.eprints.1110002024-04-18T08:17:13Z http://psasir.upm.edu.my/id/eprint/111000/ Harnessing reinforcement learning in fog-cloud computing: challenges, insights, and future directions Al-Hashimi, Mustafa Rahiman, Amir Rizaan Muhammed, Abdullah Hamid, Nor Asilah Wati The fast-changing world of fog-cloud computing poses various challenges and opportunities, especially in terms of optimizing resources, adaptability, and system efficiency. Reinforcement Learning (RL) is a powerful tool to tackle these challenges due to its ability to learn and adjust from interactions. This article explores the different RL algorithms, emphasizing their distinct strengths, weaknesses, and practical implications in fog-cloud environments. We present a comprehensive comparative analysis, from the deterministic nature of Q-Learning to the scalability of DQN and the adaptability of PPO, providing insights that can assist both practitioners and researchers. Additionally, we discuss the ethical considerations, real-world applicability, and scalability challenges associated with deploying RL in fog-cloud systems. In conclusion, while integrating RL in fog-cloud computing shows promise, it requires a comprehensive, interdisciplinary approach to ensure that advancements are ethical, efficient, and beneficial for everyone. Little Lion Scientific R&D 2024 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/111000/1/20Vol102No5.pdf Al-Hashimi, Mustafa and Rahiman, Amir Rizaan and Muhammed, Abdullah and Hamid, Nor Asilah Wati (2024) Harnessing reinforcement learning in fog-cloud computing: challenges, insights, and future directions. Journal of Theoretical and Applied Information Technology, 102 (5). pp. 1908-1919. ISSN 1992-8645; ESSN: 1817-3195 http://www.jatit.org/volumes/Vol102No5/20Vol102No5.pdf |
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The fast-changing world of fog-cloud computing poses various challenges and opportunities, especially in terms of optimizing resources, adaptability, and system efficiency. Reinforcement Learning (RL) is a powerful tool to tackle these challenges due to its ability to learn and adjust from interactions. This article explores the different RL algorithms, emphasizing their distinct strengths, weaknesses, and practical implications in fog-cloud environments. We present a comprehensive comparative analysis, from the deterministic nature of Q-Learning to the scalability of DQN and the adaptability of PPO, providing insights that can assist both practitioners and researchers. Additionally, we discuss the ethical considerations, real-world applicability, and scalability challenges associated with deploying RL in fog-cloud systems. In conclusion, while integrating RL in fog-cloud computing shows promise, it requires a comprehensive, interdisciplinary approach to ensure that advancements are ethical, efficient, and beneficial for everyone. |
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Article |
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Al-Hashimi, Mustafa Rahiman, Amir Rizaan Muhammed, Abdullah Hamid, Nor Asilah Wati |
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Al-Hashimi, Mustafa Rahiman, Amir Rizaan Muhammed, Abdullah Hamid, Nor Asilah Wati Harnessing reinforcement learning in fog-cloud computing: challenges, insights, and future directions |
author_facet |
Al-Hashimi, Mustafa Rahiman, Amir Rizaan Muhammed, Abdullah Hamid, Nor Asilah Wati |
author_sort |
Al-Hashimi, Mustafa |
title |
Harnessing reinforcement learning in fog-cloud computing: challenges, insights, and future directions |
title_short |
Harnessing reinforcement learning in fog-cloud computing: challenges, insights, and future directions |
title_full |
Harnessing reinforcement learning in fog-cloud computing: challenges, insights, and future directions |
title_fullStr |
Harnessing reinforcement learning in fog-cloud computing: challenges, insights, and future directions |
title_full_unstemmed |
Harnessing reinforcement learning in fog-cloud computing: challenges, insights, and future directions |
title_sort |
harnessing reinforcement learning in fog-cloud computing: challenges, insights, and future directions |
publisher |
Little Lion Scientific R&D |
publishDate |
2024 |
url |
http://psasir.upm.edu.my/id/eprint/111000/1/20Vol102No5.pdf http://psasir.upm.edu.my/id/eprint/111000/ http://www.jatit.org/volumes/Vol102No5/20Vol102No5.pdf |
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