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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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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