Skip to main content
SpringerLink
Log in

A forecasting-based approach for optimal deployment of edge servers in 5G networks

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The growing need for applications with low latency and high bandwidth in 5G networks has shifted the emphasis towards integrating edge computing into mobile network architectures. The optimal deployment of edge servers (ESs) is crucial for balancing performance and cost. Current research primarily focuses on the optimal deployment of ESs. However, there is limited attention on forecasting the workload of ESs, particularly in scenarios where they are placed in their optimal positions, which is crucial for accommodating anticipated variations in user densities and workload fluctuations expected in the near future. This research forecasting of workload in 5G networks by leveraging the Temporal Hierarchical Attention Mechanism Network (THAMNET) model for accurate Internet traffic forecasting. Addressing this gap, the Max–Min Fairness Allocation Scheme (MMF-AS) algorithm is implemented to ensure a balanced workload distribution. This approach facilitates fair allocation of Base Stations (BSs) to ESs, taking into account factors such as workload, distance, and connectivity. A novel approach is introduced by integrating the THAMNET forecasting model with the Particle Swarm Optimization (PSO) and MMF-AS algorithms. The proposed method considers predicted future traffic demands, and connectivity between ESs and BSs, and aims to achieve balanced workload distribution, higher utilisation rates, minimised latency, and reduced energy consumption. Experimental results demonstrate equitable workload distribution 25.95%, utilisation rate 17.57%, and notable reductions in latency 47.61% and energy consumption 32.28% compared to the non-forecasting comparison.

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
Algorithm 1
Algorithm 2
Algorithm 3
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

Similar content being viewed by others

Data availability

The data supporting this study’s findings are publicly accessible at A Data repository http://sguangwang.com/.

References

  1. Khalfi, B., Hamdaoui, B., Guizani, M.: Extracting and exploiting inherent sparsity for efficient IoT support in 5G: challenges and potential solutions. IEEE Wirel. Commun. 24(5), 68–73 (2017)

    Article  Google Scholar 

  2. Fan, X., Xiang, C., Chen, C., Yang, P., Gong, L., Song, X., Nanda, P., He, X.: BuildSenSys: reusing building sensing data for traffic prediction with cross-domain learning. IEEE Trans. Mob. Comput. 20(6), 2154–2171 (2020)

    Article  Google Scholar 

  3. Goudarzi, M., Palaniswami, M., Buyya, R.: Scheduling IoT applications in edge and fog computing environments: a taxonomy and future directions. ACM Comput. Surv. 55(7), 1–41 (2022)

    Article  Google Scholar 

  4. Kasi, S.K., Kasi, M.K., Ali, K., Raza, M., Afzal, H., Lasebae, A., Naeem, B., Ul Islam, S., Rodrigues, J.J.: Heuristic edge server placement in industrial internet of things and cellular networks. IEEE Internet Things J. 8(13), 10308–10317 (2020)

    Article  Google Scholar 

  5. Tiwari, V., Pandey, C., Dahal, A., Roy, D.S., Fiore, U.: A knapsack-based metaheuristic for edge server placement in 5G networks with heterogeneous edge capacities. Future Gener. Comput. Syst. (2023). https://doi.org/10.1016/j.future.2023.11.028

    Article  Google Scholar 

  6. Tiwari, V., Pandey, C., Roy, D.S.: Internet activity forecasting over 5G billing data using deep learning techniques. In: 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP), pp. 1–4. IEEE (2022)

  7. Lv, L., Wu, Z., Zhang, L., Gupta, B.B., Tian, Z.: An edge-AI based forecasting approach for improving smart microgrid efficiency. IEEE Trans. Ind. Inform. 18(11), 7946–7954 (2022)

    Article  Google Scholar 

  8. Pandey, C., Tiwari, V., Pattanaik, S., Roy, D.S.: A strategic metaheuristic edge server placement scheme for energy saving in smart city. In: 2023 International Conference on Artificial Intelligence and Smart Communication (AISC), pp. 288–292. IEEE (2023)

  9. Manogaran, G., Srivastava, G., Muthu, B.A., Baskar, S., Shakeel, P.M., Hsu, C.-H., Bashir, A.K., Kumar, P.M.: A response-aware traffic offloading scheme using regression machine learning for user-centric large-scale internet of things. IEEE Internet Things J. 8(5), 3360–3368 (2020)

    Article  Google Scholar 

  10. Tang, T., Li, C., Liu, F.: Collaborative cloud-edge-end task offloading with task dependency based on deep reinforcement learning. Comput. Commun. (2023). https://doi.org/10.1016/j.comcom.2023.06.021

    Article  PubMed  PubMed Central  Google Scholar 

  11. Iftikhar, S., Gill, S.S., Song, C., Xu, M., Aslanpour, M.S., Toosi, A.N., Du, J., Wu, H., Ghosh, S., Chowdhury, D., et al.: AI-based fog and edge computing: a systematic review, taxonomy and future directions. Internet Things 21, 100674 (2022)

    Article  Google Scholar 

  12. Shen, B., Xu, X., Qi, L., Zhang, X., Srivastava, G.: Dynamic server placement in edge computing toward internet of vehicles. Comput. Commun. 178, 114–123 (2021)

    Article  Google Scholar 

  13. Lin, J., Pan, L.: Multiobjective trajectory optimization with a cutting and padding encoding strategy for single-UAV-assisted mobile edge computing system. Swarm Evol. Comput. 75, 101163 (2022)

    Article  Google Scholar 

  14. Alliance, N.: 5G white paper. Next generation mobile networks, white paper 1(2015) (2015)

  15. Jabbari, G., Chalish, N., Ghiasian, A., Khorsandi Koohanestani, A.: Heterogenous server placement for delay sensitive applications in green mobile edge computing. Wirel. Pers. Commun. 126(2), 1301–1319 (2022)

    Article  Google Scholar 

  16. Somesula, M.K., Mothku, S.K., Annadanam, S.C.: Cooperative service placement and request routing in mobile edge networks for latency-sensitive applications. IEEE Syst. J. (2023). https://doi.org/10.1109/JSYST.2023.3260028

    Article  Google Scholar 

  17. Li, B., Hou, P., Wu, H., Hou, F.: Optimal edge server deployment and allocation strategy in 5G ultra-dense networking environments. Pervasive Mob. Comput. 72, 101312 (2021)

    Article  Google Scholar 

  18. Cao, B., Wei, Q., Lv, Z., Zhao, J., Singh, A.K.: Many-objective deployment optimization of edge devices for 5G networks. IEEE Trans. Netw. Sci. Eng. 7(4), 2117–2125 (2020)

    Article  MathSciNet  Google Scholar 

  19. Jia, M., Cao, J., Liang, W.: Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Trans. Cloud Comput. 5(4), 725–737 (2015)

    Article  Google Scholar 

  20. Dou, J., Yuan, F., Cao, J., Meng, X., Ma, X., Guo, Z.: Placement combination between heterogeneous services and heterogeneous capacitated servers in edge computing. J. Grid Comput. 21(1), 16 (2023)

    Article  Google Scholar 

  21. Chen, Y., Lin, Y., Zheng, Z., Yu, P., Shen, J., Guo, M.: Preference-aware edge server placement in the internet of things. IEEE Internet Things J. 9(2), 1289–1299 (2022). https://doi.org/10.1109/JIOT.2021.3079328

    Article  Google Scholar 

  22. Kasi, S.K., Kasi, M.K., Ali, K., Raza, M., Afzal, H., Lasebae, A., Naeem, B., Islam, S.U., Rodrigues, J.J.P.C.: Heuristic edge server placement in industrial internet of things and cellular networks. IEEE Internet Things J. 8(13), 10308–10317 (2021). https://doi.org/10.1109/JIOT.2020.3041805

    Article  Google Scholar 

  23. Chen, X., Jiao, L., Li, W., Fu, X.: Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Netw. 24(5), 2795–2808 (2015)

    Article  Google Scholar 

  24. Zhao, X., Zeng, Y., Ding, H., Li, B., Yang, Z.: Optimize the placement of edge server between workload balancing and system delay in smart city. Peer Peer Netw. Appl. 14, 3778–3792 (2021)

    Article  Google Scholar 

  25. Badri, H., Bahreini, T., Grosu, D., Yang, K.: Energy-aware application placement in mobile edge computing: a stochastic optimization approach. IEEE Trans. Parallel Distrib. Syst. 31(4), 909–922 (2020). https://doi.org/10.1109/TPDS.2019.2950937

    Article  Google Scholar 

  26. Ling, C., Feng, Z., Xu, L., Huang, Q., Zhou, Y., Zhang, W., Yadav, R.: An edge server placement algorithm based on graph convolution network. IEEE Trans. Veh. Technol. (2022). https://doi.org/10.1109/TVT.2022.3226681

    Article  Google Scholar 

  27. Li, Y., Wang, S.: An energy-aware edge server placement algorithm in mobile edge computing. In: 2018 IEEE International Conference on Edge Computing (EDGE), pp. 66–73. IEEE (2018)

  28. Li, B., Hou, P., Wang, K., Peng, Z., Jin, S., Niu, L.: Deployment of edge servers in 5G cellular networks. Trans. Emerg. Telecommun. Technol. 33(8), 3937 (2022)

    Article  Google Scholar 

  29. Toka, L.: Ultra-reliable and low-latency computing in the edge with Kubernetes. J. Grid Comput. 19(3), 31 (2021)

    Article  Google Scholar 

  30. Kumar, M., Sharma, S.C.: Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment. Comput. Electr. Eng. 69, 395–411 (2018)

    Article  Google Scholar 

  31. Ling, C., Feng, Z., Xu, L., Huang, Q., Zhou, Y., Zhang, W., Yadav, R.: An edge server placement algorithm based on graph convolution network. IEEE Trans. Veh. Technol. 72(4), 5224–5239 (2023). https://doi.org/10.1109/TVT.2022.3226681

    Article  Google Scholar 

  32. Sun, J., Peng, M., Jiang, H., Hong, Q., Sun, Y.: HMIAN: a hierarchical mapping and interactive attention data fusion network for traffic forecasting. IEEE Internet Things J. 9(24), 25685–25697 (2022). https://doi.org/10.1109/JIOT.2022.3196461

    Article  Google Scholar 

  33. Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2016)

    Article  MathSciNet  PubMed  Google Scholar 

  34. Li, Y., Zhou, A., Ma, X., Wang, S.: Profit-aware edge server placement. IEEE Internet Things J. 9(1), 55–67 (2021)

    Article  CAS  Google Scholar 

Download references

Funding

The authors acknowledge the Ministry of Electronics and Information Technologies (MeitY), Government of India, for supporting this research through Grant No. 13(38)/2020-CC &BT.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Meghalaya, Shillong, India

    Vaibhav Tiwari, Chandrasen Pandey & Diptendu Sinha Roy

Authors
  1. Vaibhav Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chandrasen Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Diptendu Sinha Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

VT, CP, and DSR designed the research. VT and CP conducted experimental analysis under DSR’s supervision. VT, CP, and DSR drafted the paper. All authors authorised the final draft version.

Corresponding author

Correspondence to Vaibhav Tiwari.

Ethics declarations

Competing interests

The authors of this research paper declare that they have no competing interests. There are no financial ties between the authors that could influence the study’s findings. To ensure the objectivity and integrity of the reported results, the authors conducted this study with complete transparency and adherence to ethical research practices.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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

Tiwari, V., Pandey, C. & Sinha Roy, D. A forecasting-based approach for optimal deployment of edge servers in 5G networks. Cluster Comput (2024). https://doi.org/10.1007/s10586-023-04250-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10586-023-04250-0

Keywords

Search

Navigation