Skip to main content
Log in

A multi-stage analysis of network slicing architecture for 5G mobile networks

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

Recently, the increasing demand for low latency, the explosive growth in the volume of network traffic, the large and growing number of connected devices, and diversified multimedia applications have paved the way for a new era of mobile networks. To meet these diverse requirements of different businesses in network virtualization, network slicing has emerged as a promising paradigm of upcoming 5G mobile networks. Network slicing is a major technology, based on network function virtualization and software defined network technologies, which aims to achieve more efficient utilization of available network traffic and reduce operating costs. In this paper, we propose a network slicing architecture for 5G mobile networks involving cloud radio access network (C-RAN), mobile edge computing (MEC), and cloud data center. We model the proposed network slicing system based on queueing theory, which can be used to derive the main performance metrics such as the CPU utilization, system throughput, system drop rate, average number of message requests, average response time, and average waiting time. We provide quantitative examples to show how this proposed model could be applied to estimate the system performance and cost for a network slicing system in 5G mobile networks and the number of C-RAN and MEC cores required under diverse 5G traffic conditions. The analytical results and simulation models indicate that the proposed model has a powerful ability to assign the number of C-RAN and MEC cores required to achieve the quality of service targets of 5G slices.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Choi, Y.-I., & Park, N. (2017). Slice architecture for 5G core network. In 9th International conference on ubiquitous and future networks (ICUFN), 2017 (pp. 1–7).

  2. Pérez-Romero, J. et al. (2018). On the configuration of radio resource management in a sliced RAN. In IEEE/IFIP network operations and management symposium, 2018 (pp. 1–9).

  3. Agiwal, M., Roy, A., & Saxena, N. (2016). Next generation 5G wireless networks: A comprehensive survey. IEEE Communications Surveys & Tutorials,18(3), 1617–1655.

    Article  Google Scholar 

  4. Yousaf, F. Z., et al. (2017). NFV and SDN-key technology enablers for 5G networks. IEEE Journal on Selected Areas in Communications,35(11), 2468–2478.

    Article  Google Scholar 

  5. Velasco, L., et al. (2018). An architecture to support autonomic slice networking. Journal of Lightwave Technology,36(1), 135–141.

    Article  Google Scholar 

  6. Wei, H., Zhang, Z., & Fan, B. (2017). Network slice access selection scheme in 5G. In IEEE 2nd information technology, networking, electronic and automation control conference (ITNEC), 2017 (pp. 1–8).

  7. Richart, M., et al. (2016). Resource slicing in virtual wireless networks: A survey. IEEE Transactions on Network and Service Management,13(3), 462–476.

    Article  Google Scholar 

  8. Nguyen, V.-G., et al. (2017). SDN/NFV-based mobile packet core network architectures: A survey. IEEE Communications Surveys and Tutorials,19(3), 1567–1602.

    Article  Google Scholar 

  9. Kalyoncu, F., Zeydan, E., & Yigit, I. O. (2018) A data analysis methodology for obtaining network slices towards 5G cellular networks. In IEEE 87th Vehicular Technology Conference (VTC Spring) (pp. 1–7).

  10. Afolabi, I., et al. (2017). End-to-end network slicing enabled through network function virtualization. In IEEE Conference on Standards for Communications and Networking (CSCN) (pp. 1–8).

  11. Olwal, T. O., Djouani, K., & Kurien, A. M. (2016). A survey of resource management toward 5G radio access networks. IEEE Communications Surveys & Tutorials,18(3), 1656–1686.

    Article  Google Scholar 

  12. Checko, A., et al. (2015). Cloud RAN for mobile networks—A technology overview. IEEE Communications surveys & tutorials,17(1), 405–426.

    Article  Google Scholar 

  13. Taleb, T., et al. (2017). On multi-access edge computing: A survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Communications Surveys & Tutorials,19(3), 1657–1681.

    Article  Google Scholar 

  14. Foukas, X., et al. (2017). Network slicing in 5G: Survey and challenges. IEEE Communications Magazine,55(5), 94–100.

    Article  Google Scholar 

  15. Zhang, L., et al. (2018). Filtered OFDM systems, algorithms, and performance analysis for 5G and beyond. IEEE Transactions on Communications,66(3), 1205–1218.

    Article  Google Scholar 

  16. Akihiro, N., et al. (2017). End-to-end Network Slicing for 5G Mobile Networks. Journal of Information Processing,25, 153–163.

    Article  Google Scholar 

  17. Chen, H., & Yao, D. D. (2013). Fundamentals of queuing networks: Performance, asymptotic, and optimization (Vol. 46). Berlin: Springer.

    Google Scholar 

  18. Bolch, G., et al. (2006). Queuing networks and Markov chains: Modeling and performance evaluation with computer science applications. Hoboken: Wiley.

    Book  Google Scholar 

  19. Bhat, U. N. (2015). An introduction to queuing theory: Modeling and analysis in applications. Basel: Birkhäuser.

    Book  Google Scholar 

  20. Liang, C., & Yu, F. R. (2015). Wireless network virtualization: A survey, some research issues and challenges. IEEE Communications Surveys & Tutorials,17(1), 358–380.

    Article  Google Scholar 

  21. Han, B., et al. (2018). Admission and congestion control for 5G network slicing. In IEEE Conference on Standards for Communications and Networking (CSCN) (pp. 1–9).

  22. Narmanlioglu, O., Zeydan, E., & Arslan, S. S. (2018). Service-aware multi-resource allocation in software-defined next generation cellular networks. IEEE Access,6, 20348–20363.

    Article  Google Scholar 

  23. Ye, Q., et al. (2019). End-to-end delay modeling for embedded VNF chains in 5G core networks. IEEE Internet of Things Journal,6(1), 692–704.

    Article  Google Scholar 

  24. Kurtz, F., et al. (2018). Network slicing for critical communications in shared 5G infrastructures-an empirical evaluation. In 4th IEEE Conference on Network Softwarization and Workshops (NetSoft) (pp. 1–9).

  25. Zanzi, L., & Sciancalepore, V. (2018) On guaranteeing end-to-end network slice latency constraints in 5G networks. In 15th International Symposium on Wireless Communication Systems (ISWCS) (pp. 1–8).

  26. Costanzo, S., et al. (2018). Dynamic network slicing for 5G IoT and eMBB services: A new design with prototype and implementation results. In 3rd Cloudification of the Internet of Things (CIoT) (pp. 1–9).

  27. Matthiesen, B., Aydin, O., & Jorswieck, E. A. (2018). Throughput and energy-efficient network slicing. In 22nd International ITG Workshop on Smart Antennas, 2018 (pp. 1–9).

  28. Afolabi, I., et al. (2018). Network slicing and softwarization: A survey on principles, enabling technologies, and solutions. IEEE Communications Surveys & Tutorials,20(3), 2429–2453.

    Article  Google Scholar 

  29. Toosi, A. N., et al. (2019). Management and orchestration of network slices in 5G, fog, edge and clouds (pp. 79–101). Fog and Edge Computing: Principles and Paradigms.

    Google Scholar 

  30. Sahner, R. A., Trivedi, K., & Puliafito, A. (2012). Performance and reliability analysis of computer systems: An example-based approach using the SHARPE software package. Berlin: Springer.

    Google Scholar 

  31. Nelson, R. (2013). Probability, stochastic processes, and queueing theory: The mathematics of computer performance modeling. Berlin: Springer.

    Google Scholar 

  32. Bertoli, M., Casale, G., & Serazzi, G. (2009). JMT: Performance engineering tools for system modeling. ACM SIGMETRICS Performance Evaluation Review,36(4), 10–15.

    Article  Google Scholar 

  33. Fishman, G. S. (2013). Discrete-event simulation: Modeling, programming, and analysis. Berlin: Springer.

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Research Center of College of Computer and Information Sciences, King Saud University. The authors are grateful for this support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Salman A. AlQahtani.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

AlQahtani, S.A., Alhomiqani, W.A. A multi-stage analysis of network slicing architecture for 5G mobile networks. Telecommun Syst 73, 205–221 (2020). https://doi.org/10.1007/s11235-019-00607-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-019-00607-2

Keywords

Navigation