Abstract
OptiDRL casts MEC resource allocation as a multiobjective decision-making problem, balancing latency, energy usage, and resource utilization. A DRL agent is learned in this context with a well-crafted reward function that balances these objectives. The optical integration provides ultra-low latency and high-throughput communication, further improving system efficiency. We deploy and test OptiDRL on simulation environments mimicking real-world MEC environments. Experimental results show that OptiDRL outperforms current state-of-the-art benchmark algorithms with a significant latency reduction of up to 35 %, resource saving of 25 %, and scalability improvement under changing workload scenarios. This paper proves the potential in integrating optical networking with DRL to advance intelligent MEC resource management.
Acknowledgments
Thanks to all my coauthors for the support.
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Research ethics: Na.
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Informed consent: We all are fully responsible for this paper.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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