A Method of Dynamic Resource Adjustment for 5G Network Slice

  • Qinghai Ou
  • Jigao Song
  • Yanru Wang
  • Zhiqiang Wang
  • Yang YangEmail author
  • Diya Ran
  • Lei Feng
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1143)


5G network supports multiple application scenarios with different performance requirements. In various application scenarios, the network needs to meet different delay, reliability, and speed requirements. Network slicing is the best solution to this problem. The traffic volume changes over time, which causes the resource utilization of network slices to change frequently. The static allocation of resources in the network slice may have insufficient resource utilization or resource overload. Therefore, it is necessary to dynamically adjust the resource amount of the network slice to optimize the utilization of network resources. Based on the above analysis and requirements, this paper proposes a dynamic resource adjustment scheme (DRAS) in the network slice of 5G local transmission network, in order to improve the utilization of network resources. Finally, the simulation results show that the scheme is superior to the non-dynamic resource allocation scheme (non-DRAS).


5G network Network slicing Resource allocation 



This work is supported by 2019 State Grid Science and Technology project “Analysis of Power Wireless Private Network Evolution and 4G/5G Technology Application”.


  1. 1.
    Zhang, C., Zheng, Z.: Task migration for mobile edge computing using deep reinforcement learning. Futur. Gener. Comput. Syst. 96, 111–118 (2019)CrossRefGoogle Scholar
  2. 2.
    NGMN Alliance: Description of Network Slicing Concept. Version 1.0 (2016)Google Scholar
  3. 3.
    Foukas, X., Patounas, G., Elmokashfi, A., et al.: Network slicing in 5G: survey and challenges. IEEE Commun. Mag. 55(5), 94–100 (2017)CrossRefGoogle Scholar
  4. 4.
    GPP Technical Report 23.799: Study on architecture for next generation system. Version 0.7.0 (2016)Google Scholar
  5. 5.
    Rong, B., Qian, Y., Lu, K.: Integrated downlink resource management for multiservice WiMAX networks. IEEE Trans. Mob. Comput. 6(6), 621–632 (2007)CrossRefGoogle Scholar
  6. 6.
    Ordonez-Lucena, J., Ameigeiras, P., Lopez, D., et al.: Network slicing for 5G with SDN/NFV: concepts, architectures, and challenges. IEEE Commun. Mag. 55(5), 80–87 (2017)CrossRefGoogle Scholar
  7. 7.
    Samdanis, K., Costa-Perez, X., Sciancalepore, V.: From network sharing to multi-tenancy: The 5G network slice broker. IEEE Commun. Mag. 54(7), 32–39 (2016)CrossRefGoogle Scholar
  8. 8.
    NGMN Alliance 5G white paper. 2015-2-17. 5G White Paper V10.pdf. (2016-1120)
  9. 9.
    Alliance, N.: 5G white paper. Next generation mobile networks, white paper (2015)Google Scholar
  10. 10.
    Davy, S., Famaey, J., Serrat-Fernandez, J., et al.: Challenges to support edge-as-a-service. Commun. Mag. IEEE 52(1), 132–139 (2014)CrossRefGoogle Scholar
  11. 11.
    Fukuhara, S., Tachibana, T.: Robustness-based resource trading with optimization problem for network slicing. In: 2017 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), pp. 337–338. IEEE (2017)Google Scholar
  12. 12.
    Kamel, M.I., Long, B.L., Girard, A.: LTE wireless network virtualization: dynamic slicing via flexible scheduling. In: Vehicular Technology Conference, pp. 1–5. IEEE (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Qinghai Ou
    • 1
  • Jigao Song
    • 1
  • Yanru Wang
    • 1
  • Zhiqiang Wang
    • 2
  • Yang Yang
    • 3
    Email author
  • Diya Ran
    • 3
  • Lei Feng
    • 3
  1. 1.Beijing Fibrlink Communications Co., Ltd.BeijingChina
  2. 2.State Grid Shanxi Electric Power CompanyXi’anChina
  3. 3.State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina

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