Skip to main content

Advertisement

SpringerLink
Log in

Low-latency AP handover protocol and heterogeneous resource scheduling in SDN-enabled edge computing

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

Abstract

As mobile devices are widely used and various applications emerge, users have higher demands on data rates and computing power. Software Defined Network (SDN) can configure and manage various devices in the network through a centralized control controller, making the network more flexible. In an SDN-enabled edge computing environment, dense multiple access devices make mobile devices handover frequently, and mobile devices handover between different access points becomes an inevitable problem. To address this problem, we propose an Access Point (AP) handover strategy based on the signal strength and traffic load. The scheme uses the global view and centralized control capability of the SDN controller to obtain, manage, and analyze information, then calculate the weights and compare them, and finally develop the handover policy. On the other hand, to improve system resource utilization and meet the performance demands of different applications, MEC systems need to allocate computing and communication resources appropriately to keep users' Quality-of-Service (QoS) experience. We propose a joint optimization strategy for computing and communication resources based on the Lagrange multiplier method. The policy calculates and analyzes the task execution latency and energy consumption of edge servers and local terminals, and develops an optimization scheme for sub-channel allocation and resource allocation. It aims to reduce latency and energy consumption as much as possible. The results of the experiments in this paper illustrate that the proposed AP handover scheme which is on the basis of received signal strength indicator (RSSI) and traffic load can effectively improve the task completion time and energy consumption performance. The proposed joint optimization strategy of computing and communication resources based on the Lagrange multiplier method can effectively improve energy consumption and delay performance.

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data availability

No associated data.

References

  1. Mao, Y., You, C., Zhang, J., et al. (2017). A survey on mobile edge computing: The communication perspective[J]. IEEE Communications Surveys & Tutorials, 19, 2322–2358.

    Article  Google Scholar 

  2. Ab Ba, S. N., Yan, Z., Taherkordi, A., et al. (2017). Mobile edge computing: A survey[J]. IEEE Internet of Things Journal, 5, 450–465.

    Google Scholar 

  3. Zishu, L. I., Xie, R., Sun, L., et al. (2018). A survey of mobile edge computing[J]. Telecommunications Science, 34, 87–101.

    Google Scholar 

  4. Li, C., Liang, S. Y., Zhang, J., Wang, Q.-E., & Luo, Y. (2022). Blockchain-based data trading in edge-cloud computing environment[J]. Information Processing and Management, 59(1), 102786.

    Article  Google Scholar 

  5. Chouhan, D., Gautam, N., Purohit, G., et al. (2021). A survey on virtualization techniques in mobile edge computing[J]. WEENTECH Proceedings in Energy. https://doi.org/10.32438/WPE.412021

    Article  Google Scholar 

  6. Li, C., Zhang, Y., & Luo, Y. (2022). Intermediate data placement and cache replacement strategy under Spark platform[J]. Journal of Parallel and Distributed Computing, 163, 114–135.

    Article  Google Scholar 

  7. Li, C., Zhang, Y., Gao, X., et al. (2022). Energy-latency tradeoffs for edge caching and dynamic service migration based on DQN in mobile edge computing[J]. Journal of Parallel and Distributed Computing, 166, 15–31.

    Article  Google Scholar 

  8. Liu, J., Zhang, L., Li, C., Bai, J., Lv, H., & Lv, Z. (2022). Blockchain-based secure communication of intelligent transportation digital twins system. IEEE Transactions on Intelligent Transportation Systems, 23(11), 22630–22640.

    Article  Google Scholar 

  9. Karakus, M., Durresi, A., et al. (2017). A survey: Control plane scalability issues and approaches in software-defined networking (SDN)[J]. Computer Networks, 112, 279–293.

    Article  Google Scholar 

  10. Benzekki, K., Fergougui, A. E., & Elalaoui, A. E. (2016). Software-defined networking (SDN): A survey[J]. Security & Communication Networks, 9(18), 5803–5833.

    Article  Google Scholar 

  11. Li, C., Cai, Q., & Youlong, L. (2022). Lowlatency edge cooperation caching based on base station cooperation in SDN based MEC. Expert Systems with Applications, 191, 116–252.

    Article  Google Scholar 

  12. Li, C., Cai, Q., & Youlong, L. (2022). Optimal data placement strategy considering capacity limitation and load balancing in geographically distributed cloud[J]. Future Generation Computer Systems, 127, 100–111.

    Article  Google Scholar 

  13. Liu, J., Zhang, L., Li, C., et al. (2022). Blockchain-based secure communication of intelligent transportation digital twins system[J]. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2022.3183379

    Article  Google Scholar 

  14. 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 

  15. Li, C., Zhang, Y., & Luo, Y. (2022). A federated learning-based edge caching approach for mobile edge computing-enabled intelligent connected vehicles. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2022.3224395

    Article  Google Scholar 

  16. 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 

  17. Oktian, Y. E., Lee, S. G., Lee, H. J., et al. (2017). Distributed SDN controller system: A survey on design choice[J]. Computer Networks, 121(5), 100–111.

    Article  Google Scholar 

  18. Aldhaibani, O. A., Al-Jumaili, M. H., Raschella, A., et al. (2021). A centralized architecture for autonomic quality of experience oriented handover in dense networks[J]. Computers & Electrical Engineering, 94(2), 107352.

    Article  Google Scholar 

  19. Wu, X., & Haas, H. (2019). Handover skipping for LiFi[J]. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2903409

    Article  Google Scholar 

  20. Gilani, S. M. M., Hong, T., Jin, W., et al. (2017). Mobility management in IEEE 802.11 WLAN using SDN/NFV technologies[J]. Eurasip Journal on Wireless Communications & Networking, 2017(1), 67.

    Article  Google Scholar 

  21. Chen, Z., Manzoor, S., Gao, Y. et al. (2017). Achieving load balancing in high-density software defined WiFi networks[C]. In 2017 International conference on frontiers of information technology (FIT). IEEE Computer Society.

  22. Ding, K., Wang, X., Zhang, G., et al. (2017). A flow-based authentication handover mechanism for multi-domain SDN mobility environment[J]. Wireless communication over ZigBee for automotive inclination measurement. China Communications, 14(9), 127–143.

    Article  Google Scholar 

  23. Kiran, N., Yin, C., Akram, Z. (2017). AP load balance based handover in software defined WiFi systems[C]. In 2016 IEEE international conference on network infrastructure and digital content (IC-NIDC). IEEE.

  24. Zhang, S. W., Wang, F. L., Yuan, C. J. (2019). Handover management strategy based on active state of 5G ultra-dense network users[J]. In Communications technology.

  25. Gharsallah, A., Zarai, F., & Neji, M. (2019). SDN/NFV-based handover management approach for ultradense 5G mobile networks[J]. International Journal of Communication Systems, 32, e3831.

    Article  Google Scholar 

  26. Ji, L., Hui, G., Lv, T. et al. (2018). Deep reinforcement learning based computation offloading and resource allocation for MEC[C]. In 2018 IEEE wireless communications and networking conference (WCNC). IEEE.

  27. Xue, J., & An, Y. (2021). Joint task offloading and resource allocation for multi-task multi-server NOMA-MEC networks[J]. IEEE Access, 9, 16152–16163.

    Article  Google Scholar 

  28. Wu, Y. C., Dinh, T. Q., Fu, Y., et al. (2021). A hybrid DQN and optimization approach for strategy and resource allocation in MEC networks[J]. IEEE Transactions on Wireless Communications, 20, 4282–4295.

    Article  Google Scholar 

  29. Wu, X., Jiang, W., Zhang, Y., et al. (2019). Online combinatorial based mechanism for MEC network resource allocation[J]. International Journal of Communication Systems, 32(7), e3928.1-e3928.16.

    Article  Google Scholar 

  30. Zhang, H., Wang, Z., & Liu, K. (2020). V2X offloading and resource allocation in SDN-assisted MEC-based vehicular networks[J]. China Communications, 17(5), 266–283.

    Article  Google Scholar 

  31. Aghdam, B., & Shaghaghi, K. R. (2021). Effective resource allocation and load balancing in hierarchical hetnets: Toward QoS-aware multi-access edge computing[J]. The Computer Journal, 66, 229–244.

    Google Scholar 

  32. Du, Y. (2021). Deep reinforcement learning for computation offloading and resource allocation in unmanned-aerial-vehicle assisted edge computing[J]. Sensors, 21, 6499.

    Article  Google Scholar 

  33. Al-Razgan, M., Alfakih, T., & Hassan, M. M. (2021). A computational offloading method for edge server computing and resource allocation management[J]. Journal of Mathematics, 2021, 1–11.

    Article  Google Scholar 

  34. Mustafa, N., Mahmood, W., Chaudhry, A. A., Ibrahim, C. M. (2005). Pre-scanning and dynamic caching for fast handoff at MAC layer in IEEE 802.11 wireless LANs. In IEEE International conference on mobile adhoc and sensor systems conference, pp. 8–122. https://doi.org/10.1109/MAHSS.2005.1542783.

  35. Lin, C., Tsai, W., Tsai, M., Cai, Y. (2017). Adaptive load-balancing scheme through wireless SDN-based association control. In 2017 IEEE 31st international conference on advanced information networking and applications (AINA), pp. 546–553. https://doi.org/10.1109/AINA.2017.16.

  36. Wang, C., Liang, C., Yuv, F. R. et al. (2017). Joint computation offloading, resource allocation and content caching in cellular networks with mobile edge computing[C]. In Icc IEEE international conference on communications. IEEE.

  37. Dab, B., Aitsaadi, N., Langar, R. (2019). Joint optimization of offloading and resource allocation scheme for mobile edge computing[C]. In 2019 IEEE wireless communications and networking conference (WCNC). IEEE.

Download references

Acknowledgements

The work was supported by by the National Natural Science Foundation ofChina (NSFC) under grants(No.62171330), Open Fund of Key Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs(No. 2022KLABA05) , Open Fund of Anhui Institute of Territorial Space Planning and Ecology (No.GTY2021101), Open Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province( No. 22Kftk05),.

Author information

Authors and Affiliations

  1. Anhui Institute of Territorial Space Planning and Ecology, Anhui Jianzhu University, Hefei, People’s Republic of China

    Chunlin Li

  2. Key Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs, Beijing, People’s Republic of China

    Chunlin Li

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

    Chunlin Li, Zhiqiang Yu, Yong Zhang & Youlong Luo

  4. Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang, People’s Republic of China

    Zhiqiang Yu

  5. State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System, Baotou, People’s Republic of China

    Xinyong Li & Libin Zhang

Authors
  1. Chunlin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiqiang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinyong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Libin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yong Zhang
    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

Corresponding author

Correspondence to Chunlin Li.

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., Yu, Z., Li, X. et al. Low-latency AP handover protocol and heterogeneous resource scheduling in SDN-enabled edge computing. Wireless Netw 29, 2171–2187 (2023). https://doi.org/10.1007/s11276-023-03302-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-023-03302-y

Keywords

Search

Navigation