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Abstract
With the development of the Internet of Things, research on edge computing has surged. The essence of edge computing is to bring processing closer to data sources, aiming to minimize latency and enhance efficiency. However, resource constraints, network bandwidth limitations, and dynamic demands in edge computing present optimization challenges. Traditional reinforcement learning methods require manual feature engineering and can’t automatically learn advanced features, making them unsuitable for high-dimensional states and complex decision-making. To address these challenges, this paper investigates above question in edge networks, developing a model based on Multi-Agent Deep Q-Learning (MA-DQN). It introduces a self-learning offloading strategy where each user acts independently, observes its local environment, and optimally offloads without knowing other users’ conditions. Simulation results demonstrate that this network minimizes system utility, approaching the optimal solution.
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References
El-Sayed, H., et al.: Edge of things: the big picture on the integration of edge, IoT, and the cloud in a distributed computing environment. IEEE Access6, 1706–1717 (2017)
Caprolu, M., Di Pietro, R., Lombardi, F., Raponi, S.: Edge computing perspectives: architectures, technologies, and open security issues, pp. 116–123 (2019)
Huang, D., Wang, P., Niyato, D.: A dynamic offloading algorithm for mobile computing. IEEE Trans. Wirel. Commun.11(6), 1991–1995 (2012)
Chiang, M., Zhang, T.: Fog and IoT: an overview of research opportunities. IEEE Internet Things J.3(6), 854–864 (2016)
Huang, L., Bi, S., Zhang, Y.J.A.: Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks. IEEE Trans. Mob. Comput.19(11), 2581–2593 (2019)
Tran, T.X., Pompili, D.: Joint task offloading and resource allocation for multi-server mobile-edge computing networks. IEEE Trans. Veh. Technol.68(1), 856–868 (2018)
Guo, S., Xiao, B., Yang, Y., Yang, Y.: Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing. In: IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9. IEEE (2016)
Chen, M.H., Liang, B., Dong, M.: Joint offloading decision and resource allocation for multi-user multi-task mobile cloud. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2016)
Xi, L., Sun, M., Zhou, H., Xu, Y., Wu, J., Li, Y.: Multi-agent deep reinforcement learning strategy for distributed energy. Measurement185, 109,955 (2021)
Huang, L., Feng, X., Qian, L., Wu, Y.: Deep reinforcement learning-based task offloading and resource allocation for mobile edge computing. In: Meng, L., Zhang, Y. (eds.) MLICOM 2018. LNICST, vol. 251, pp. 33–42. Springer, Cham (2018).https://doi.org/10.1007/978-3-030-00557-3_4
Chen, X.: Decentralized computation offloading game for mobile cloud computing. IEEE Trans. Parallel Distrib. Syst.26(4), 974–983 (2014)
Xu, Y., Zhang, H., Ji, H., Yang, L., Li, X., Leung, V.C.: Transaction throughput optimization for integrated blockchain and MEC system in IoT. IEEE Trans. Wirel. Commun.21(2), 1022–1036 (2021)
He, H., Zhang, S., Zeng, Y., Zhang, R.: Joint altitude and beamwidth optimization for UAV-enabled multiuser communications. IEEE Commun. Lett.22(2), 344–347 (2017)
Wu, H., Wolter, K., Jiao, P., Deng, Y., Zhao, Y., Xu, M.: EEDTO: an energy-efficient dynamic task offloading algorithm for blockchain-enabled IoT-edge-cloud orchestrated computing. IEEE Internet Things J.8(4), 2163–2176 (2020)
Li, H.: Multi-task offloading and resource allocation for energy-efficiency in mobile edge computing. Int. J. Comput. Tech.5(1), 5–13 (2018)
Wen, Y., Zhang, W., Luo, H.: Energy-optimal mobile application execution: taming resource-poor mobile devices with cloud clones. In: 2012 Proceedings IEEE INFOCOM, pp. 2716–2720. IEEE (2012).https://doi.org/10.1109/INFCOM.2012.6195626
Bertsekas, D.: Dynamic Programming and Optimal Control: Volume I, vol. 4. Athena scientific (2012)
Acknowledgment
This paper was supported by Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project) (Research and Application of Industry Oriented Large-scale Cloud Native Application Architecture Support Platform) (No. 2020CXGC010110).
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Authors and Affiliations
Shandong University of Finance and Economics, Jinan, 250014, Shandong, China
Jiatai Wang & Yunfeng Zhang
Beijing University of Posts and Telecommunications, Beijing, 100876, China
Jiaen Zhou & Ronghui Zhang
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- Yunfeng Zhang
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Correspondence toYunfeng Zhang.
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Peking University, Beijing, China
Ying Tan
Southern University of Science and Technology, Shenzhen, China
Yuhui Shi
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Wang, J., Zhou, J., Zhang, R., Zhang, Y. (2024). Edge Dynamic Service Offloading Based on Multi-agent Deep Q Learning. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2024. Lecture Notes in Computer Science, vol 14789. Springer, Singapore. https://doi.org/10.1007/978-981-97-7184-4_28
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