Abstract
Edge computing is proving to be a promising model, offering low-latency and high-bandwidth services to the end-users. However, due to the dynamic nature of the network and the heterogeneous computing resources, task scheduling in edge clouds remains a challenging problem. In order to solve this problem, we propose a novel task scheduling algorithm for edge clouds based on deep reinforcement learning, which combines a deep Q-learning network with a priority-based action selection strategy. This approach aims to optimize computing resource allocation while minimizing energy consumption in edge nodes. We evaluated the effectiveness of our algorithm using a simulated edge cloud environment and compared it with other advanced task scheduling algorithms. Experimental results indicate that our algorithm outperforms baseline algorithms in terms of delay and energy consumption. In particular, our method improves task completion time and energy efficiency compared to traditional scheduling algorithms.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (62272256), Shandong Provincial Natural Science Foundation (No. ZR2021MF026), the Piloting Fundamental Research Program of Qilu University of Technology (Shandong Academy of Sciences) (2022XD001, and the Colleges and Universities 20 Terms Foundation of Jinan City China (202228903).
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Xun, M., Yao, Y., Yu, J., Zhang, H., Feng, S., Cao, J. (2024). Deep Reinforcement Learning for Delay and Energy-Aware Task Scheduling in Edge Clouds. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2012. Springer, Singapore. https://doi.org/10.1007/978-981-99-9637-7_32
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DOI: https://doi.org/10.1007/978-981-99-9637-7_32
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