Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Adaptive joint placement of edge intelligence services in mobile edge computing

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

Abstract

Mobile edge computing is an energy-efficient paradigm which has great support for resource-limited user devices performing compute-intensive programs and applications. However, limited edge resources, mobility of user equipment, growth of service requirements, and dynamic nature of service types make it a challenging task to configure computing and storage resources for executing various services on edge servers. Therefore, an adaptive joint service placement framework in the edge system with various devices of user and mobile edge servers is proposed in this paper. The proposed framework takes into account the mobility of user devices, the dynamic changes in various types of services, the cost of service placement and service usage, and optimizes the service placement scheme from the perspective of different target groups. Simultaneously, we design a deep deterministic policy gradient based service placement tuning approach in which centralized critic networks and actor networks are jointly used to improve the service placement performance. The relative evaluation results validate the effectiveness of the proposed framework and approach in improving the performance of the edge system.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Chen, Y., Zhang, S., Jin, Y., Qian, Z., Xiao, M., Ge, J., & Lu, S. (2022). Locus: User-perceived delay-aware service placement and user allocation in MEC environment. IEEE Transactions on Parallel and Distributed Systems, 33(7), 1581–1592. https://doi.org/10.1109/TPDS.2021.3119948

    Article  Google Scholar 

  2. Bhatta, D., & Mashayekhy, L. (2022). A bifactor approximation algorithm for cloudlet placement in edge computing. IEEE Transactions on Parallel and Distributed Systems, 33(8), 1787–1798. https://doi.org/10.1109/TPDS.2021.3126256

    Article  Google Scholar 

  3. Lin, M., Wierman, A., Andrew, L. L. H., & Thereska, E. (2013). Dynamic right-sizing for power-proportional data centers. IEEE/ACM Transactions on Networking, 21(5), 1378–1391. https://doi.org/10.1109/TNET.2012.2226216

    Article  Google Scholar 

  4. Ma, X., Wang, S., Zhang, S., Yang, P., Lin, C., & Shen, X. (2021). Cost-efficient resource provisioning for dynamic requests in cloud assisted mobile edge computing. IEEE Transactions on Cloud Computing, 9(3), 968–980. https://doi.org/10.1109/TCC.2019.2903240

    Article  Google Scholar 

  5. Wang, S., Zhao, Y., Xu, J., Yuan, J., & Hsu, C.-H. (2019). Edge server placement in mobile edge computing. Journal of Parallel and Distributed Computing, 127, 160–168. https://doi.org/10.1016/j.jpdc.2018.06.008

    Article  Google Scholar 

  6. He, Z., Li, K., & Li, K. (2022). Cost-efficient server configuration and placement for mobile edge computing. IEEE Transactions on Parallel and Distributed Systems, 33(9), 2198–2212. https://doi.org/10.1109/TPDS.2021.3135955

    Article  Google Scholar 

  7. Lu, J., Jiang, J., Balasubramanian, V., Khosravi, M. R., & Xu, X. (2022). Deep reinforcement learning-based multi-objective edge server placement in internet of vehicles. Computer Communications, 187, 172–180. https://doi.org/10.1016/j.comcom.2022.02.011

    Article  Google Scholar 

  8. Jiang, X., Hou, P., Zhu, H., Li, B., Wang, Z., & Ding, H. (2023). Dynamic and intelligent edge server placement based on deep reinforcement learning in mobile edge computing. Ad Hoc Networks, 145, 103172. https://doi.org/10.1016/j.adhoc.2023.103172

    Article  Google Scholar 

  9. Mada, B.E., Bagaa, M., Tale, T., Flinck, H. (2020). Latency-aware service placement and live migrations in 5g and beyond mobile systems. In ICC 2020 - 2020 IEEE International Conference on Communications (ICC). pp. 1–6 https://doi.org/10.1109/ICC40277.2020.9148940

  10. Moubayed, A., Shami, A., Heidari, P., Larabi, A., & Brunner, R. (2021). Edge-enabled v2x service placement for intelligent transportation systems. IEEE Transactions on Mobile Computing, 20(4), 1380–1392. https://doi.org/10.1109/TMC.2020.2965929

    Article  Google Scholar 

  11. Ghobaei-Arani, M., & Shahidinejad, A. (2022). A cost-efficient IoT service placement approach using whale optimization algorithm in fog computing environment. Expert Systems with Applications, 200, 117012. https://doi.org/10.1016/j.eswa.2022.117012

    Article  Google Scholar 

  12. Talpur, A., & Gurusamy, M. (2022). DRLD-SP: A deep-reinforcement-learning-based dynamic service placement in edge-enabled internet of vehicles. IEEE Internet of Things Journal, 9(8), 6239–6251. https://doi.org/10.1109/JIOT.2021.3110913

    Article  Google Scholar 

  13. Ouyang, T., Chen, X., Zhou, Z., Li, R., & Tang, X. (2023). Adaptive user-managed service placement for mobile edge computing via contextual multi-armed bandit learning. IEEE Transactions on Mobile Computing, 22(3), 1313–1326. https://doi.org/10.1109/TMC.2021.3106746

    Article  Google Scholar 

  14. Ouyang, T., Zhou, Z., & Chen, X. (2018). Follow me at the edge: Mobility-aware dynamic service placement for mobile edge computing. IEEE Journal on Selected Areas in Communications, 36(10), 2333–2345. https://doi.org/10.1109/JSAC.2018.2869954

    Article  Google Scholar 

  15. Chen, X., Pu, L., Gao, L., Wu, W., & Wu, D. (2017). Exploiting massive D2D collaboration for energy-efficient mobile edge computing. IEEE Wireless Communications, 24(4), 64–71. https://doi.org/10.1109/MWC.2017.1600321

    Article  Google Scholar 

  16. Abdulla, M., Steinmetz, E., Wymeersch, H. (2016). Vehicle-to-vehicle communications with urban intersection path loss models. In 2016 IEEE Globecom Workshops (GC Wkshps), pp. 1–6 https://doi.org/10.1109/GLOCOMW.2016.7849078

  17. Cai, C., Wang, Q., & Xiao, W. (2022). Mixed sub-fractional Brownian motion and drift estimation of related Ornstein-Uhlenbeck process. Communications in Mathematics and Statistics. https://doi.org/10.1007/s40304-021-00245-8

    Article  Google Scholar 

  18. Al-Eryani, Y., Akrout, M., & Hossain, E. (2021). Multiple access in cell-free networks: Outage performance, dynamic clustering, and deep reinforcement learning-based design. IEEE Journal on Selected Areas in Communications, 39(4), 1028–1042. https://doi.org/10.1109/JSAC.2020.3018825

    Article  Google Scholar 

  19. Gao, A., Geng, T., Ng, S. X., & Liang, W. (2021). A continuous policy learning approach for hybrid offloading in backscatter communication. IEEE Communications Letters, 25(2), 523–527. https://doi.org/10.1109/LCOMM.2020.3026312

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the MIIT of China 2020 (Identification Resources Search System for Industrial Internet of Things) and National Key Research and Development Program of China (No.2018YFB1800502). The corresponding author is Ru Huo.

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Lei Du & Ru Huo

  2. Department of Automation, Tsinghua University, Beijing, 100084, China

    Chuang Sun

  3. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Shuo Wang & Tao Huang

Authors
  1. Lei Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ru Huo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chuang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tao Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ru Huo.

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

Du, L., Huo, R., Sun, C. et al. Adaptive joint placement of edge intelligence services in mobile edge computing. Wireless Netw 30, 799–817 (2024). https://doi.org/10.1007/s11276-023-03520-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-023-03520-4

Keywords

Search

Navigation