پژوهشنامه پردازش و مدیریت اطلاعات

پژوهشنامه پردازش و مدیریت اطلاعات

Network Slicing for Customizing 5G Networks for Industry-Specific Needs

نوع مقاله : مقاله پژوهشی

نویسندگان
Mustafa M. Zayer 1
Zaid Ghanim Ali 2
Bazieva Aliia Mansurovna 3
Salima Baji Abdullah 4
Matai NagiSaeed 5
1 Al-Turath University, Baghdad 10013, Iraq,
2 Al-Mansour University College, Baghdad 10067, Iraq
3 Osh State University, Osh City 723500, Kyrgyzstan
4 Al-Rafidain University College Baghdad 10064, Iraq
5 Madenat Alelem University College, Baghdad 10006, Iraq
10.22034/jipm.2025.728121
چکیده
ABSTRACT
Background: Network slicing has turned out to be one of the key enablers in the 5G networks due to the ability to support the diverse applications such as ultra reliable and low latency communications for the self-driving cars or IoT-like massive machine type communications. Prior expeditions lacked integrated tools for the dynamic assignment and allocation of resources and no possibility for maintaining constant QoS.
Objective: In this article, the primary aim is to synthesis and test a reinforcement learning–driven slicing framework in order to orchestrate the resources of the three types of slices – URLLC, mMTC, and eMBB. This is to improve the performance of the sliced resource, ensure high availability, and minimize competition of the resources in multi-tenant scenarios in 5G networks.
Methods: The proposed study design includes a focus on the key stakeholders and their needs for requirements gathering and an experimental field for actual implementation. Resource distribution is guided by the reinforcement learning algorithms by trying to minimize a cost function which incorporates the relation between the latency, isolation, throughput and energy expended. Using a number of runs, quality of performance is monitored to enable assessment of stability as well as response rates.
Results: Experimental results show that the proposed framework achieves a lower level of latency violations and capacity oversubscription compared to heuristic methods. Furthermore, it consistently achieves nearly 2.5X better throughput for telemedicine slices and guarantees less than 5 ms latency for time-sensitive services during dynamic traffic conditions.
Conclusion: The study shows how reinforcement learning can be effective and applied for end-to-end 5G network slicing. This sort of adaptive orchestration can increase service dependability while optimising overhead and herald instantly climbable multi-tenant networks compatible with various industries
کلیدواژه‌ها
5G
network slicing
industry-specific networks
customization
virtualization
low-latency
orchestration
slice isolation
autonomous systems
telecommunications

عنوان مقاله English

Network Slicing for Customizing 5G Networks for Industry-Specific Needs

نویسندگان English

Mustafa M. Zayer 1
Zaid Ghanim Ali 2
Bazieva Aliia Mansurovna 3
Salima Baji Abdullah 4
Matai NagiSaeed 5
1 Al-Turath University, Baghdad 10013, Iraq,
2 Al-Mansour University College, Baghdad 10067, Iraq
3 Osh State University, Osh City 723500, Kyrgyzstan
4 Al-Rafidain University College Baghdad 10064, Iraq
5 Madenat Alelem University College, Baghdad 10006, Iraq
چکیده English

ABSTRACT
Background: Network slicing has turned out to be one of the key enablers in the 5G networks due to the ability to support the diverse applications such as ultra reliable and low latency communications for the self-driving cars or IoT-like massive machine type communications. Prior expeditions lacked integrated tools for the dynamic assignment and allocation of resources and no possibility for maintaining constant QoS.
Objective: In this article, the primary aim is to synthesis and test a reinforcement learning–driven slicing framework in order to orchestrate the resources of the three types of slices – URLLC, mMTC, and eMBB. This is to improve the performance of the sliced resource, ensure high availability, and minimize competition of the resources in multi-tenant scenarios in 5G networks.
Methods: The proposed study design includes a focus on the key stakeholders and their needs for requirements gathering and an experimental field for actual implementation. Resource distribution is guided by the reinforcement learning algorithms by trying to minimize a cost function which incorporates the relation between the latency, isolation, throughput and energy expended. Using a number of runs, quality of performance is monitored to enable assessment of stability as well as response rates.
Results: Experimental results show that the proposed framework achieves a lower level of latency violations and capacity oversubscription compared to heuristic methods. Furthermore, it consistently achieves nearly 2.5X better throughput for telemedicine slices and guarantees less than 5 ms latency for time-sensitive services during dynamic traffic conditions.
Conclusion: The study shows how reinforcement learning can be effective and applied for end-to-end 5G network slicing. This sort of adaptive orchestration can increase service dependability while optimising overhead and herald instantly climbable multi-tenant networks compatible with various industries

کلیدواژه‌ها English

5G
network slicing
industry-specific networks
customization
virtualization
low-latency
orchestration
slice isolation
autonomous systems
telecommunications

References

Afolabi, I., Bagaa, M., Boumezer, W., and Taleb, T. (2021). Toward a Real Deployment of Network Services Orchestration and Configuration Convergence Framework for 5G Network Slices.  IEEE Network, 35 (1), 242-250. https://doi.org/:10.1109/MNET.011.2000146
Afrianto, Y., Sukoco, H., and Wahjuni, S. (2018). Weighted Round Robin Load Balancer to Enhance Web Server Cluster in OpenFlow Networks.  TELKOMNIKA (Telecommunication Computing Electronics and Control).
Afrianto, Y., and Wahjuni, S. (2018). Weighted Round Robin Load Balancer to Enhance Web Server Cluster in OpenFlow Networks.  Telkomnika (Telecommunication Computing Electronics and Control), 16, 1402-1408. https://doi.org/:10.12928/TELKOMNIKA.v16i3.5601
Ageyev, D., Yarkin, D., and Nameer, Q. (2014). Traffic aggregation and EPS network planning problem. First International Scientific-Practical Conference Problems of Infocommunications Science and Technology, 14-17 Oct. https://doi.org/:10.1109/INFOCOMMST.2014.6992316.
Azimi, Y., Yousefi, S., Kalbkhani, H., and Kunz, T. (2022). Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey.  IEEE Access, 10, 106581-106612. https://doi.org/:10.1109/ACCESS.2022.3210254
Babbar, H., Rani, S., AlZubi, A. A., Singh, A., Nasser, N., and Ali, A. (2022). Role of Network Slicing in Software Defined Networking for 5G: Use Cases and Future Directions.  IEEE Wireless Communications, 29 (1), 112-118. https://doi.org/:10.1109/MWC.001.2100318
Chen, M., Yang, J., Hao, Y., Mao, S., and Hwang, K. (2017). A 5G Cognitive System for Healthcare. Big Data and Cognitive Computing, 1 (1). https://doi.org/:10.3390/bdcc1010002.
Escolar, A. M., Alcaraz-Calero, J. M., Salva-Garcia, P., Bernabe, J. B., and Wang, Q. (2021). Adaptive Network Slicing in Multi-Tenant 5G IoT Networks.  IEEE Access, 9, 14048-14069. https://doi.org/:10.1109/ACCESS.2021.3051940
Felici-Castell, S., García-Pineda, M., Segura-Garcia, J., Fayos-Jordan, R., and Lopez-Ballester, J. (2021). Adaptive live video streaming on low-cost wireless multihop networks for road traffic surveillance in smart cities.  Future Generation Computer Systems, 115, 741-755. https://doi.org/:https://doi.org/10.1016/j.future.2020.10.010
Foukas, X., Patounas, G., Elmokashfi, A., and Marina, M. K. (2017). Network Slicing in 5G: Survey and Challenges. IEEE Communications Magazine, 55 (5), 94-100. https://doi.org/:10.1109/MCOM.2017.1600951
Hussain, F., Hussain, R., Anpalagan, A., and Benslimane, A. (2020). A New Block-Based Reinforcement Learning Approach for Distributed Resource Allocation in Clustered IoT Networks.  IEEE Transactions on Vehicular Technology, 69 (3), 2891-2904. https://doi.org/:10.1109/TVT.2020.2965796
Khan, A. A., Abolhasan, M., Ni, W., Lipman, J., and Jamalipour, A. (2021). An End-to-End (E2E) Network Slicing Framework for 5G Vehicular Ad-Hoc Networks.  IEEE Transactions on Vehicular Technology, 70 (7), 7103-7112. https://doi.org/:10.1109/TVT.2021.3084735
Khanh, Q. V., Hoai, N. V., Manh, L. D., Le, A. N., and Jeon, G. (2022). Wireless Communication Technologies for IoT in 5G: Vision, Applications, and Challenges.  Wireless Communications and Mobile Computing, 2022, 3229294. https://doi.org/:10.1155/2022/3229294
Ksentini, A., and Frangoudis, P. A. (2020). Toward Slicing-Enabled Multi-Access Edge Computing in 5G. IEEE Network, 34 (2), 99-105. https://doi.org/:10.1109/MNET.001.1900261
Lieto, A., Malanchini, I., Mandelli, S., Moro, E., and Capone, A. (2022). Strategic Network Slicing Management in Radio Access Networks.  IEEE Transactions on Mobile Computing, 21 (4), 1434-1448. https://doi.org/:10.1109/TMC.2020.3025027
Luo, Y., Jiang, M., Zhang, D., and Effenberger, F. (2023). Field Trial of Network Slicing in 5G and PON-Enabled Industrial Networks.  IEEE Wireless Communications, 30 (1), 78-85. https://doi.org/:10.1109/MWC.002.2200215
Mai, T., Yao, H., Zhang, N., He, W., Guo, D., and Guizani, M. (2022). Transfer Reinforcement Learning Aided Distributed Network Slicing Optimization in Industrial IoT.  IEEE Transactions on Industrial Informatics, 18 (6), 4308-4316. https://doi.org/:10.1109/TII.2021.3132136
Nota, A., Saidi, S., Overbeck, D., Kurtz, F., and Wietfeld, C. (2022). Providing Response Times Guarantees for Mixed-Criticality Network Slicing in 5G. Design, Automation & Test in Europe Conference & Exhibition (DATE), 14-23 March. https://doi.org/:10.23919/DATE54114.2022.9774503.
Polyakov, N., Yarkina, N., and Samouylov, K. (2021). A simulator for analyzing a network slicing policy with SLA-based performance isolation of slices.  Discrete and Continuous Models and Applied Computational Science, 29, 36-52. https://doi.org/:10.22363/2658-4670-2021-29-1-36-52
Polyakov N.A., Y. N. V., Samouylov K.E. (2021). A simulator for analyzing a network slicing policy with SLA-based performance isolation of slices.  Discrete and Continuous Models and Applied Computational Science  29 (1), 36-52. https://doi.org/10.22363/2658-4670-2021-29-1-36-52
Qasim, N., Jawad, A., Jawad, H., Khlaponin, Y., and Nikitchyn, O. (2022). Devising a traffic control method for unmanned aerial vehicles with the use of gNB-IOT in 5G.  Eastern-European Journal of Enterprise Technologies, 3, 53-59. https://doi.org/:10.15587/1729-4061.2022.260084
Salih, M. M., Khaleel, B. M., Qasim, N. H., Ahmed, W. S., Kondakova, S., and Abdullah, M. Y. (2024). Capacity, Spectral and Energy Efficiency of OMA and NOMA Systems. 35th Conference of Open Innovations Association (FRUCT). https://doi.org/:10.23919/FRUCT61870.2024.10516394.
Sieliukov A.V., Q. N. H., Khlaponin Y.I. (2022). Conceptual model of the mobile communication network.  The Workshop on Emerging Technology Trends on the Smart Industry and the Internet of Things «TTSIIT», 20-22.
Singh, S., Babu, C. R., Ramana, K., Ra, I.-H., and Yoon, B. (2022). BENS−B5G: Blockchain-Enabled Network Slicing in 5G and Beyond-5G (B5G) Networks. Sensors, 22 (16). https://doi.org/:10.3390/s22166068.
Wang, H., Wu, Y., Min, G., and Miao, W. (2022). A Graph Neural Network-Based Digital Twin for Network Slicing Management.  IEEE Transactions on Industrial Informatics, 18 (2), 1367-1376. https://doi.org/:10.1109/TII.2020.3047843
Wu, Y., Dai, H. N., Wang, H., Xiong, Z., and Guo, S. (2022). A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory.  IEEE Communications Surveys & Tutorials, 24 (2), 1175-1211. https://doi.org/:10.1109/COMST.2022.3158270
Yadav, A. K., Wijethilaka, S., Braeken, A., Misra, M., and Liyanage, M. (2023). An Enhanced Cross-Network-Slice Authentication Protocol for 5G.  IEEE Transactions on Sustainable Computing, 8 (4), 555-573. https://doi.org/:10.1109/TSUSC.2023.3283615
Yan, D., Ng, B. K., Ke, W., and Lam, C. T. (2023). Deep Reinforcement Learning Based Resource Allocation for Network Slicing With Massive MIMO.  IEEE Access, 11, 75899-75911. https://doi.org/:10.1109/ACCESS.2023.3296851

  • تاریخ دریافت 12 مرداد 1404