Abstract
Edge computing is a very good choice to solve the shortage of computing power of mobile devices (MD), the bandwidth resources and computing power of edge servers are also limited, therefore, efficient task offloading strategies and resource scheduling can reduce task computing time, reduce system energy consumption and improve user experience. In this study, a three-layer network architecture is proposed, namely a “end-edge-cloud” three-layer network architecture. Secondly, considering the maximum tolerance delay of user service, a comprehensive optimization model for task offloading and resource allocation aiming at the minimum energy consumption of the system is designed. The model clearly defines the state space, action space and reward function of Markov stochastic process, and adopts the simulation based on double Q-network (DDQN) and dueling deep Q-network (Dueling DQN). Experimental results show that the proposed scheme is better than multiple baseline algorithms and the original DQN algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
LNCS Homepage. http://www.springer.com/lncs. Accessed 20 Jul 2023
Shi, W., Cao, J., Zhang, Q., et al.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2018)
Leduc, S., Cantor, Z., Kelly, P., et al.: LO15: Paramedic and allied health professional interventions at long-term care facilities to reduce emergency department visits: systematic review. Can. J. Emerg. Med. 22(S1), S12–S12 (2020)
Mach, P., Becvar, Z.: Edge computing: a survey on architecture and computation offloading. IEEE Commun. Surv. Tut. 19(3), 1628–1656 (2017)
Liu, B., Liu, C., Peng, M.: Resource allocation for energy-efficient MEC in NOMA-enabled massive IoT networks. IEEE J. Sel. Areas Commun. 39(4), 1015–1027 (2021)
Li, Y., Wang, X., et al.: Learning-aided computation offloading for trusted collaborative mobile edge computing. IEEE Trans. Mobile Comput. 19(12), 2833–2849 (2020)
Wang, Z., Zhao, Z., Min, G., et al.: User mobility aware task assignment for mobile edge computing. Futur. Gener. Comput. Syst. 85, 1–8 (2018)
Wei, Z., Zhao, B., Su, J., et al.: Dynamic edge computation offloading for internet of things with energy harvesting: a learning method. IEEE Internet Things J. 6(3), 4436–4447 (2018)
Zhang, W., Zhang, Z., et al.: MASM: a multiple-algorithm service model for energy-delay optimization in edge artificial intelligence. IEEE Trans. Ind. Informat. 15(7), 4216–4224 (2019)
Jeon, Y., Baek, H., Pack, S.: Mobility-aware optimal task offloading in distributed edge computing. In: 2021 International Conference on Information Networking (ICOIN), pp. 65–68. IEEE (2021)
Acknowledgment
This work was supported in part by China Postdoctoral Science Foundation, Special Fund for Epidemic prevention and control of COVID-19 Fund (No. 2020T130129ZX ), the Postdoctoral Research Fund of Jiangsu Province (No. 2019K086),the High-Level Talent Foundation of **ling Institute of Technology (No.JIT-B-201703, JIT-RCYJ-201802), 2019 school-level research fund incubation project (No. Jit-fhxm-201912), the Postdoctoral Research Fund of Jiangsu Province (No. 2019K086), “Qinglan Project” of Jiangsu Province.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hui, X., Ningling, M., Wenting, H., **anjun, Z. (2024). Mobile Edge Computing Offloading Strategy Based on Deep Reinforcement Learning. 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_20
Download citation
DOI: https://doi.org/10.1007/978-981-99-9637-7_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9636-0
Online ISBN: 978-981-99-9637-7
eBook Packages: Computer ScienceComputer Science (R0)
