Jicheng Kan
ABSTRACT
This article considers a multiple Node server and multiple user scenario and proposes a joint user association and power allocation optimization scheme to minimize power consumption and queuing delay. Firstly, a network and computational unloading model is established based on the comprehensive consideration of random task arrival and time-varying wireless channels. Then, the optimization goal is to minimize the average long-term service cost. A dynamic computing unloading and resource allocation algorithm based on mixed decision deep reinforcement learning is proposed for this research objective. By calling the Actor part of DDPG and combining the Critc part of DDPG with D3QN, the mixed decision problem in edge node scenarios is solved. The proposed algorithm has better stability and faster convergence compared to baseline algorithms such as DQN. At the same time, under different task arrival rates, the average system service cost of the proposed algorithm is significantly reduced.
- Gabay, M. , & Zaourar, S. . 2016. Vector bin packing with heterogeneous bins: application to the machine reassignment problem. Annals of Operations Research, 242(1), 161-194.Google Scholar
Cross Ref
- Fricker, C. , Guillemin, F. , Robert, P. , & Thompson, G. . 2015. Analysis of an offloading scheme for data centers in the framework of fog computing. Acm Transactions on Modeling & Performance Evaluation of Computing Systems, 1(4), 16..Google Scholar
- Desikan, K. S., Srinivasan, M., & Murthy, C. S. R. 2017, July. A novel distributed latency-aware data processing in fog computing-enabled IoT networks. In Proceedings of the ACM workshop on distributed information processing in wireless networks (pp. 1-6).Google ScholarDigital Library
- Han, Z., Qi, H., Chang, D., Gong, W., & Dai, S. 2022, February. Research on optimization method of routing buffer linkage based on Q-learning. In Journal of Physics: Conference Series (Vol. 2187, No. 1, p. 012058). IOP Publishing.Google Scholar
- Min, M., Xiao, L., Chen, Y., Cheng, P., Wu, D., & Zhuang, W. 2019. Learning-based computation offloading for IoT devices with energy harvesting. IEEE Transactions on Vehicular Technology, 68(2), 1930-1941.Google Scholar
- Zhang, G., Ni, S., & Zhao, P. 2021. Learning-based joint optimization of energy delay and privacy in multiple-user edge-cloud collaboration MEC systems. IEEE Internet of Things Journal, 9(2), 1491-1502.Google Scholar
- Dai Y , K Zhang , Maharjan S , 2020. Edge Intelligence for Energy -efficient Computation unloading and Resource Allocation in 5G Beyond [J]. IEEE Transactions on Vehicular Technology , PP (99):11.Google ScholarCross Ref
- Li, M., Yu, F. R., Si, P., Wu, W., & Zhang, Y. 2020. Resource optimization for delay-tolerant data in blockchain-enabled IoT with edge computing: A deep reinforcement learning approach. IEEE Internet of Things Journal, 7(10), 9399-9412.Google Scholar
- Tang, M., & Wong, V. W. 2020. Deep reinforcement learning for task offloading in mobile edge computing systems. IEEE Transactions on Mobile Computing, 21(6), 1985-1997.Google Scholar
- Hu, H., Wu, D., Zhou, F., Jin, S., & Hu, R. Q. 2021, December. Dynamic task offloading in MEC-enabled IoT networks: A hybrid DDPG-D3QN approach. In 2021 IEEE global communications conference (GLOBECOM) (pp. 1-6). IEEE.Google Scholar
- Kan, J., & Zhou, X. 2022, December. Research on Edge-Computing for Independent task assignment based on deep reinforcement learning. In 2022 10th International Conference on Information Systems and Computing Technology (ISCTech) (pp. 621-627). IEEE.Google Scholar
- Lu H, He X, Du M, 2020. Edge QoE: Computation unloading with Deep Reinforcement Learning for Internet of Things[J].IEEE Internet of Things Journal, PP(99):1-1.Google Scholar
- Zhang, J., Du, J., Shen, Y., & Wang, J. 2020. Dynamic computation offloading with energy harvesting devices: A hybrid-decision-based deep reinforcement learning approach. IEEE Internet of Things Journal, 7(10), 9303-9317.Google Scholar
- Chen, Z., & Wang, X. 2020. Decentralized computation offloading for multi-user mobile edge computing: A deep reinforcement learning approach. EURASIP Journal on Wireless Communications and Networking, 2020(1), 1-21.Google Scholar
- Lu H, He X, Du M, 2020. Edge QoE: Computation unloading with Deep Reinforcement Learning for Internet of Things[J].IEEE Internet of Things Journal, PP(99):1-1.Google Scholar
- Zhang, J., Du, J., Shen, Y., & Wang, J. 2020. Dynamic computation offloading with energy harvesting devices: A hybrid-decision-based deep reinforcement learning approach. IEEE Internet of Things Journal, 7(10), 9303-9317.Google Scholar
Index Terms
- Edge computing dynamic unloading based on deep reinforcement learning
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