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PDRAS: Priority-Based Dynamic Resource Allocation Scheme in 5G Network Slicing

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Abstract

In 5G network slicing environments, if insufficient resources are allocated to the associated resource-intensive service slices, its quality of service (QoS) can be considerably degraded. In this paper, we propose a priority-based dynamic resource allocation scheme (PDRAS), in which a resource management agent maintains slicing information such as priorities, demand profiles, and average resource adjustment time to change allocated resources to slices. To maximize QoS of slices while maintaining the total amount of allocated resources below a certain level, a constrained Markov decision process problem is formulated and the optimal allocation policy is obtained using linear programming. Extensive evaluation results demonstrate that PDRAS with the optimal policy has better performance regarding QoS and the resource usage efficiency compared with other schemes.

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Notes

  1. The resource adjustment time can include the transmission latency of the command message and resource adjustment latency in the network entities.

  2. The detailed concepts of mathematical framework (MDP/CMDP) are explained in the technical report [6].

  3. Instead of converting the formulated CMDP problem into the LP problem, we can exploit model-based and model-free learning. However, to apply these learning methods to the constrained problem, we need to introduce Lagrangian multiplier and find an appropriate value of the multiplier according to the environment [7, 8], which can cause another burden to implement our algorithm to real systems.

  4. The resource management agent does not allocate resources used by the other slices to a specific one. Note that the total summation of allocated resources to slices can be maintained below a certain level by the constraint function which will be defined in Sect. 5.

  5. Various resources from different entities should be shared in a network slicing environment, which indicates that the resource management agent commands to all entities sharing resources. Therefore, the resource adjustment time in network slicing environments can be enlarged compared to the resource sharing in virtualization of computing resources on a personal computer.

  6. Because no previous work considers the resource adjustment time, we could not find the reference for the distribution of this time.

  7. The ratio can represent how much resources have been allocated compared to demand.

  8. As shown in the definition of QoS function (i.e., \(\ln \left( {\frac{{R }}{{D}}} \right) \)), when the demand is larger than the currently allocated resource, the QoS can be negative.

  9. The constraint function will be used to make a constraint on the equivalent LP model with the formulated CMDP model.

  10. Note that, when the allocated resources R is larger than the demand D, \(\ln \left( {\frac{{R}}{{D}}} \right) \) is a positive value.

References

  1. 3GPP TR 28.801: Study on management and orchestration of network slicing for next generation network (Release 15), version 15.1.0, Jan (2018)

  2. Chiha, A., Wee, M., Colle, D., Verbrugge, S.: Network slicing cost allocation model. J. Netw. Syst. Manag. 28, 627–659 (2020)

    Article  Google Scholar 

  3. Ksentini, Adlen, Nikaein, Navid: Toward enforcing network slicing on RAN: flexibility and resources abstraction. IEEE Commun. Mag. 55(6), 102–108 (2017)

    Article  Google Scholar 

  4. Camelo, M., Cominardi, L., Gramaglia, M., Fiore, M., Garcia-Saavedra, A., Fuentes, L., Vleeschauwer, D., Soto, P., Slamnik-Krijestorac, N., Ballesteros, J., Chang, C., Baldoni, G., Marquez-Barja, J., Hellinckx, P., Latre, S.: Requirements and Specifications for the Orchestration of Network Intelligence in 6G. In: Proc. IEEE CCNC 2022 Workshop, Jan (2022)

  5. Ko, H., Lee, J., Pack, S.: Priority-based dynamic resource allocation scheme in network slicing. In: Proc. SoftNET 2021 (Conjunction with ICOIN 2021), Jan (2021)

  6. Ko, H., Lee, J., Pack, S.: Technical report: priority-based dynamic resource allocation scheme in network slicing. In: Technical Report, Oct (2021). https://shorturl.at/mtwUY

  7. Zhang, W., Wang, Q., Liu, X., Liu, Y., Chen, Y.: Three-dimension trajectory design for multi-UAV wireless network with deep reinforcement learning. IEEE Trans. Vehic. Technol. (TVT) 70(1), 600–612 (2021)

    Article  Google Scholar 

  8. He, H., Shan, H., Huang, A., Ye, Q., Zhuang, W.: Edge-aided computing and transmission scheduling for LTE-U-enabled IoT. IEEE Trans. Wirel. Commun. (TWC) 19(12), 7881–7896 (2020)

    Google Scholar 

  9. Li, T., Zhu, X., Liu, X.: An end-to-end network slicing algorithm based on deep Q-learning for 5G network. IEEE Access 8, 122229–122240 (2020)

    Article  Google Scholar 

  10. Abiko, Y., Saito, T., Ikeda, D., Ohta, K., Mizuno, T., Mineno, H.: Flexible resource block allocation to multiple slices for radio access network slicing using deep reinforcement learning. IEEE Access 8, 68183–68198 (2020)

    Article  Google Scholar 

  11. Messaoud, S., Bradai, A., Ahmed, O., Quang, P., Atri, M., Hossain, M.: Deep federated Q-learning-based network slicing for industrial IoT. IEEE Trans. Ind. Inf. (TII) (to appear)

  12. Hua, Y., Li, R., Zhao, Z., Chen, X., Zhang, H.: GAN-powered deep distributional reinforcement learning for resource management in network slicing. IEEE J. Select. Areas Commun. (JSAC) 38(2), 334–349 (2020)

    Article  Google Scholar 

  13. Wang, H., Wu, Y., Min, G., Miao, W.: A graph neural network-based digital twin for network slicing management. IEEE Trans. Ind. Inf. (TII) (to appear)

  14. Huynh, N., Hoang, D., Nguyen, D., Dutkiewicz, E.: Optimal and fast real-time resource slicing with deep dueling neural networks. IEEE J. Select. Areas Commun. (JSAC) 37(6), 1455–1470 (2019)

    Article  Google Scholar 

  15. Sciancalepore, V., Samdanis, K., Costa-Perez, X., Bega, D., Gramaglia, M., Banchs, A.: Mobile traffic forecasting for maximizing 5G network slicing resource utilization. In: Proc. IEEE INFOCOM 2017, May (2017)

  16. Bega, D., Gramaglia, M., Garcia-Saavedra, A., Fiore, M., Banchs, A., Costa-Perez, X.: Network slicing meets artificial intelligence: an AI-based framework for slice management. IEEE Commun. Mag. 58(6), 32–38 (2020)

    Article  Google Scholar 

  17. Halabian, H.: Distributed resource allocation optimization in 5G virtualized networks. IEEE J. Select. Areas Commun. (JSAC) 37(3), 627–642 (2019)

    Article  Google Scholar 

  18. Han, Y., Tao, X., Zhang, X., Jia, S.: Hierarchical resource allocation in multi-service wireless networks with wireless network virtualization. IEEE Trans. Vehic. Technol. (TVT) 69(10), 11811–11827 (2020)

    Article  Google Scholar 

  19. Feng, J., Pei, Q., Yu, F., Chu, X., Du, J., Zhu, L.: Dynamic network slicing and resource allocation in mobile edge computing systems. IEEE Trans. Vehic. Technol. (TVT) 69(7), 7863–7878 (2020)

    Article  Google Scholar 

  20. Tran, T., Le, L.: Resource allocation for multi-tenant network slicing: a multi-leader multi-follower Stackelberg game approach. IEEE Trans. Vehic. Technol. (TVT) 69(8), 8886–8899 (2020)

    Article  Google Scholar 

  21. Richart, M., Baliosian, J., Serrat, J., Gorricho, J., Aguero, R.: Slicing in WiFi networks through airtime? Based resource allocation. J. Netw. Syst. Manag. 27, 784–814 (2019)

    Article  Google Scholar 

  22. Lieto, A., Malanchini, I., Mandelli, S., Moro, E., Capone, A.: Strategic Network Slicing Management in Radio Access Networks. IEEE Trans. Mobile Comput. (TMC) (to appear)

  23. Guan, W., Zhang, H., Leung, V.: Slice reconfiguration based on demand prediction with dueling deep reinforcement learning. In: Proc. IEEE GLOBCOM 2020, Dec (2020)

  24. Fossati, F., Moretti, S., Perny, P., Secci, S.: Multi-resource allocation for network slicing. IEEE/ACM Trans. Netw. (ToN) 28(3), 1311–1324 (2020)

    Article  Google Scholar 

  25. Chien, H., Lin, Y., Lai, C., Wang, C.: End-to-end slicing with optimized communication and computing resource allocation in multi-tenant 5G systems. IEEE Trans. Vehic. Technol. (TVT) 69(2), 11811–11827 (2020)

    Google Scholar 

  26. Afolabi, I., Prados-Garzon, J., Bagaa, M., Taleb, T., Ameigeiras, P.: Dynamic resource provisioning of a scalable E2E network slicing orchestration system. IEEE Trans. Mobile Comput. (TMC) 19(11), 2594–2608 (2020)

    Article  Google Scholar 

  27. Camelo, M., Claeys, M., Latre, S.: Parallel reinforcement learning with minimal communication overhead for IoT environments. IEEE Internet Things J. 7(2), 1387–1400 (2020)

    Article  Google Scholar 

  28. NGMN Alliance: 5G White Paper. https://www.ngmn.org/uploads/media/NGMN_5G_White_Paper_V1_0.pdf

  29. Ordonez-Lucena, J., Ordonez-Lucena, J., Ameigeiras, P., Lopez, D., Ramos-Munoz, J., Lorca, J., Folgueira, J.: Network slicing for 5G with SDN/NFV: concepts, architectures, and challenges. IEEE Commun. Mag. 55(5), 80–87 (2017)

    Article  Google Scholar 

  30. Kurtz, F., Kurtz, F., Bektas, C., Dorsch, N., Wietfeld, C.: Network slicing for critical communications in shared 5G infrastructures—an empirical evaluation. In: Proc. IEEE NetSoft 2018, Jun (2018)

  31. Ko, H., Pack, S., Leung, V.: Coverage-guaranteed and energy-efficient participant selection strategy in mobile crowdsensing. IEEE Internet Things J. 6(2), 3202–3211 (2019)

    Article  Google Scholar 

  32. Law of Diminishing Marginal Utility, Madhav University (2021). https://madhavuniversity.edu.in/law-of-diminishing-marginal-utility.html

  33. Easterlin, R.A.: Diminishing marginal utility of income? Caveat emptor. Soc. Indicat. Res. 70(3), 243–255 (2004)

    Article  Google Scholar 

  34. Wikipedia, Chapman? Kolmogorov equation. https://en.wikipedia.org/wiki/Chapman?Kolmogorov_equation

  35. Wikipedia, Linear Programming. Accessed 13 Dec 2020. https://en.wikipedia.org/wiki/Linear_programming

  36. Sun, G., Gebrekidan, Z., Boateng, G., Ayepah-Mensah, D., Jiang, W.: Dynamic reservation and deep reinforcement learning based autonomous resource slicing for virtualized radio access networks. IEEE Access 7, 45758–45772 (2019)

    Article  Google Scholar 

  37. Shi, Y., Sagduyu, Y., Erpek, T.: Reinforcement learning for dynamic resource optimization in 5G radio access network slicing. In: Proc. IEEE International Workshop on CAMAD 2020, Sep (2020)

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Acknowledgements

This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-01015) and in part by the ITRC (Information Technology Research Center) support program (IITP-2022-2021-0-01810).

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Authors and Affiliations

  1. Department of Computer and Information Science, Korea University, Sejong, Korea

    Haneul Ko

  2. Electronics and Telecommunications Research Institute, Daejon, Korea

    Jaewook Lee

  3. School of Electrical Engineering, Korea University, Seoul, Korea

    Sangheon Pack

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  1. Haneul Ko
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Correspondence to Sangheon Pack.

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Ko, H., Lee, J. & Pack, S. PDRAS: Priority-Based Dynamic Resource Allocation Scheme in 5G Network Slicing. J Netw Syst Manage 30, 68 (2022). https://doi.org/10.1007/s10922-022-09681-5

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