Skip to main content
SpringerLink
Log in

Energy–latency tradeoffs edge server selection and DQN-based resource allocation schemes in MEC

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

This paper discusses the challenges of mobile edge computing with low latency and energy consumption caused by the explosive growth of communication traffic and data generated by mobile devices. To address the issue of selecting edge servers, a multi-user-oriented edge server selection strategy is proposed. The strategy minimizes the weighted sum of network latency and total energy consumption and develops an improved SPEA2-based algorithm to choose the most suitable edge server. The resource allocation problem is also considered, and a resource allocation strategy is proposed that maximizes the energy efficiency ratio. A DQN-based resource allocation strategy is devised to find the best resource allocation strategy. Experimental results demonstrate that the proposed server selection strategy reduces system overhead in terms of energy consumption and latency while improving resource utilization. The resource allocation strategy improves the computational efficiency of edge servers while reducing latency and total energy consumption.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Availability of data and materials

No associated data.

References

  1. Liu, J., Li, C., Bai, J., Luo, Y., Lv, H., & Lv, Z. (2023). Security in IoT-enabled digital twins of maritime transportation systems. IEEE Transactions on Intelligent Transportation Systems, 24(2), 2359–2367.

    Google Scholar 

  2. Lyu, N. (2021). Brief review on computing resource allocation algorithms in mobile edge computing. https://doi.org/10.1109/cds52072.2021.00025.

  3. Xu, J., Lin, J., Li, Y., & Xu, Z. (2023). MultiFed: A fast converging federated learning framework for services QoS prediction via cloud–edge collaboration mechanism. Knowledge-Based Systems, 268, 110463. https://doi.org/10.1016/j.knosys.2023.110463

    Article  Google Scholar 

  4. Chen, J., Xing, H., Xiao, Z., Xu, L., & Tao, T. (2021). A DRL agent for jointly optimizing computation offloading and resource allocation in MEC. IEEE Internet of Things Journal, 8(24), 17508–17524. https://doi.org/10.1109/jiot.2021.3081694

    Article  Google Scholar 

  5. Alqarni, M., Cherif, A., & Alkayyal, E. (2023). ODM-BCSA: An offloading decision-making framework based on binary cuckoo search algorithm for mobile edge computing. Computer Networks, 226, 109647. https://doi.org/10.1016/j.comnet.2023.109647

    Article  Google Scholar 

  6. Wu, Y.-C., Dinh, T. Q., Fu, Y., Lin, C., & Quek, T. Q. S. (2021). A hybrid DQN and optimization approach for strategy and resource allocation in MEC networks. IEEE Transactions on Wireless Communications, 20(7), 4282–4295. https://doi.org/10.1109/twc.2021.3057882

    Article  Google Scholar 

  7. Fang, F., Wang, K., Ding, Z., & Leung, V. C. M. (2021). Energy-efficient resource allocation for NOMA-MEC networks with imperfect CSI. IEEE Transactions on Communications, 69(5), 3436–3449. https://doi.org/10.1109/tcomm.2021.3058964

    Article  Google Scholar 

  8. Li, C., Qianqian, C., & Luo, Y. (2022). Low-latency edge cooperation caching based on base station cooperation in SDN based MEC. Expert Systems with Applications, 191, 116252. https://doi.org/10.1016/j.eswa.2021.116252

    Article  Google Scholar 

  9. Xu, X., Zhao, Y., Tao, L., & Xu, Z. (2021). Resource allocation strategy for dual UAVs-assisted MEC system with hybrid solar and RF energy harvesting. https://doi.org/10.1109/iccci51764.2021.9486814.

  10. Liu, J., Zhang, L., Li, C., Bai, J., Lv, H., & Lv, Z. (2022), Blockchain-based secure communication of intelligent transportation digital twins system. In IEEE transactions on intelligent transportation systems (Vol. 23, no. 11, pp. 22630–22640). https://doi.org/10.1109/TITS.2022.3183379.

  11. Lee, M., & Ko, I.-Y. (2021). Service consumption planning for efficient service migration in mobile edge computing environments. (Paper presented at the proceedings of the 36th annual ACM symposium on applied computing, virtual event, Republic of Korea).

  12. Li, C., Zhang, Y., Gao, X., & Luo, Y. (2022). Energy-latency tradeoffs for edge caching and dynamic service migration based on DQN in mobile edge computing. Journal of Parallel and Distributed Computing, 166, 15–31. https://doi.org/10.1016/j.jpdc.2022.03.001

    Article  Google Scholar 

  13. Zhang, Z., Wu, G., & Ren, H. (2021). Multi-attribute-based QoS-aware virtual network function placement and service chaining algorithms in smart cities. Computers & Electrical Engineering, 96, 107465. https://doi.org/10.1016/j.compeleceng.2021.107465

    Article  Google Scholar 

  14. Truong, T. V., & Nayyar, A. (2023). System performance and optimization in NOMA mobile edge computing surveillance network using GA and PSO. Computer Networks, 223, 109575. https://doi.org/10.1016/j.comnet.2023.109575

    Article  Google Scholar 

  15. Truong, V.-T., Ha, D.-B., Truong, T.-V., & Nayyar, A.. (2022). Performance analysis of RF energy harvesting NOMA mobile edge computing in multiple devices IIoT networks. In Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering (pp. 62–76). Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. https://doi.org/10.1007/978-3-031-08878-0_5.

  16. Zhang, R., Wu, L., Cao, S., Hu, X., Xue, S., Wu, D., et al. (2021). Task offloading with task classification and offloading nodes selection for MEC-enabled IoV. ACM Transactions on Internet Technolology, 22(2), 51. https://doi.org/10.1145/3475871

    Article  Google Scholar 

  17. Li, R., Li, X., Xu, J., Jiang, F., Jia, Z., Shao, D., et al. (2021). Energy-aware decision-making for dynamic task migration in MEC-based unmanned aerial vehicle delivery system. Concurrency and Computation: Practice and Experience, 33(22), e6092.

    Article  Google Scholar 

  18. Li, C., Zhang, Y., & Luo, Y. (2023). A federated learning-based edge caching approach for mobile edge computing-enabled intelligent connected vehicles. IEEE Transactions on Intelligent Transportation Systems, 24(3), 3360–3369.

    Article  Google Scholar 

  19. Qin, Z., Wang, H., Wei, Z., Qu, Y., Xiong, F., Dai, H., et al. (2021). Task selection and scheduling in UAV-enabled MEC for reconnaissance with time-varying priorities. IEEE Internet of Things Journal, 8(24), 17290–17307.

    Article  Google Scholar 

  20. Shi, T., Cai, Z., Li, J., & Gao, H. (2020) CROSS: a crowdsourcing based sub-servers selection framework in D2D enhanced MEC architecture. In 2020 IEEE 40th international conference on distributed computing systems (ICDCS) (pp. 1134–1144). IEEE.

  21. Natarajan, S., Khandelwal, T., & Mittal, M. (2020) MEC enabled cell selection for micro-operators based 5G open network deployment. In 2020 IEEE wireless communications and networking conference workshops (WCNCW). IEEE, pp. 1–5.

  22. Zou, G., Qin, Z., Deng, S., Li, K.-C., Gan, Y., & Zhang, B. (2021). Towards the optimality of service instance selection in mobile edge computing. Knowledge-Based Systems, 217, 106831.

    Article  Google Scholar 

  23. Tang, L., Tang, B., Zhang, L., Guo, F., & He, H. (2021). Joint optimization of network selection and task offloading for vehicular edge computing. Journal of Cloud Computing, 10(1), 1–13.

    Google Scholar 

  24. Gao, B., Zhou, Z., Liu, F., Xu, F., & Li, B. (2021). An online framework for joint network selection and service placement in mobile edge computing. IEEE Transactions on Mobile Computing, 21(11), 3836–3851.

    Article  Google Scholar 

  25. Xu, J., Zheng, R., Yang, L., Liu, M., Song, J., Zhang, M., et al. (2022). Service placement strategy for joint network selection and resource scheduling in edge computing. The Journal of Supercomputing, 78(12), 14504.

    Article  Google Scholar 

  26. Gong, C., Wei, L., Gong, D., Li, T., & Feng, F. (2022). Energy-efficient task migration and path planning in UAV-enabled mobile edge computing system. Complexity.

  27. Zhang, M., Huang, H., Rui, L., Hui, G., Wang, Y., & Qiu, X. (2020) A service migration method based on dynamic awareness in mobile edge computing. In NOMS 2020–2020 IEEE/IFIP network operations and management symposium. IEEE, pp. 1–7.

  28. Rjoub, G., Wahab, O. A., Bentahar, J., & Bataineh, A. (2022). Trust-driven reinforcement selection strategy for federated learning on IoT devices. Computing, 1–23.

  29. Li, J., Gao, H., Lv, T., & Lu, Y. (2018) Deep reinforcement learning based computation offloading and resource allocation for MEC. In 2018 IEEE wireless communications and networking conference (WCNC) (pp. 1–6). IEEE.

  30. Yu, J.-J., Zhao, M., Li, W.-T., Liu, D., Yao, S., & Feng, W. (2020) Joint offloading and resource allocation for time-sensitive multi-access edge computing network. In 2020 IEEE wireless communications and networking conference (WCNC) (pp. 1–6). IEEE.

  31. Chengze, Z., Meng, L., Enchang, S., Ru, H., Yu, L., & Yanhua, Z. (2022). Computation offloading and resource allocation for UAV-assisted IoT based on blockchain and mobile edge computing. High Technology Letters, 28(1), 80–90.

    Google Scholar 

  32. Wang, Qun., Hu, H., & Hu, R. Q. (2020). Secure and energy-efficient offloading and resource allocation in a NOMA-based MEC network. In 2020 IEEE/ACM symposium on edge computing (SEC) (pp. 420–424). IEEE.

  33. Wang, Jiadai., Zhao, L., Liu, J., & Kato, N. (2019). Smart resource allocation for mobile edge computing: A deep reinforcement learning approach. IEEE Transactions on emerging topics in computing, 9(3), 1529–1541.

    Article  Google Scholar 

  34. Tan, G., Zhang, H., & Zhou, S. (2020). Resource allocation in MEC-enabled vehicular networks: A deep reinforcement learning approach. In IEEE INFOCOM 2020-IEEE conference on computer communications workshops (INFOCOM WKSHPS) (pp. 406–411). IEEE.

  35. Wang, Pengfei., Yao, C., Zheng, Z., Sun, G., & Song, L. (2018). Joint task assignment, transmission, and computing resource allocation in multilayer mobile edge computing systems. IEEE Internet of Things Journal, 6(2), 2872–2884.

    Article  Google Scholar 

  36. Šlapak, E., Gazda, J., Guo, W., Maksymyuk, T., & Dohler, M. (2021). Cost-effective resource allocation for multitier mobile edge computing in 5G mobile networks. IEEE Access, 9, 28658–28672.

    Article  Google Scholar 

  37. Li, J.-Y., Du, K.-J., Zhan, Z.-H., Wang, H., & Zhang, J. (2022). Distributed differential evolution with adaptive resource allocation. IEEE transactions on cybernetics.

  38. Laboni, N. M., Safa, S. J., Sharmin, S., Razzaque, M. A., Rahman, M. M., & Hassan, M. M. (2022). A hyper heuristic algorithm for efficient resource allocation in 5G mobile edge clouds. IEEE Transactions on Mobile Computing.

  39. Deng, W., Ni, H., Liu, Y., Chen, H., & Zhao, H. (2022). An adaptive differential evolution algorithm based on belief space and generalized opposition-based learning for resource allocation. Applied Soft Computing, 127, 109419.

    Article  Google Scholar 

  40. Li, C., Zhang, Y., & Luo, Y. (2023). DQN-enabled content caching and quantum ant colony-based computation offloading in MEC. Applied Soft Computing, 133, 109900.

    Article  Google Scholar 

Download references

Acknowledgements

The work was supported by the National Natural Science Foundation (NSF) under grants (No. 62171330), Key Research and Development Plan of Hubei Province (2023BAB015), project of Key Laboratory of Water Grid Project and Regulation of Ministryof Water Resources(No. QTKS0034), Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, (No. YQ23201). Hubei Key Laboratory of Base Water Security (No. CX2023K14), the open Foundation of Intelligent Manufacturing Fujian University Application Technology Engineering Center (No.ZNZZ23-01).

Funding

No.

Author information

Authors and Affiliations

  1. Car Clean Energy Fujian University Application Technology Engineering Center, Quanzhou Vocational and Technical University, Quanzhou, People’s Republic of China

    Chunlin Li

  2. Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, Guilin, 541004, People’s Republic of China

    Chunlin Li & Cong Hu

  3. Key Laboratory of Water Grid Project and Regulation of Ministryof Water Resources, Changjiang Institute of Survey, Palanning, Design and Research (CISPDR) Corporation, Wuhan, 430010, Hubei, People’s Republic of China

    Chunlin Li & Qiang Liu

  4. Hubei Key Laboratory of Basin Water Security, Wuhan, People’s Republic of China

    Chengwei Lu & Youlong Luo

  5. Department of Computer Science, Wuhan University of Technology, Wuhan, 430063, People’s Republic of China

    Chunlin Li, Zewu Ke & Youlong Luo

Authors
  1. Chunlin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zewu Ke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qiang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cong Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chengwei Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Youlong Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Chunlin Li, Zewu Ke, Qiang Liu, Cong Hu, Chengwei Lu, Youlong Luo designed the study, developed the methodology, performed the analysis, and wrote the manuscript. Chunlin Li, Zewu Ke collected the data.

Corresponding author

Correspondence to Chunlin Li.

Ethics declarations

Competing interests

No Competing interests.

Ethical Approval

No applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, C., Ke, Z., Liu, Q. et al. Energy–latency tradeoffs edge server selection and DQN-based resource allocation schemes in MEC. Wireless Netw 29, 3637–3663 (2023). https://doi.org/10.1007/s11276-023-03426-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-023-03426-1

Keywords

Search

Navigation