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Network Slicing Cost Allocation Model

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Abstract

Within the upcoming fifth generation (5G) mobile networks, a lot of emerging technologies, such as Software Defined Network (SDN), Network Function Virtualization (NFV) and network slicing are proposed in order to leverage more flexibility, agility and cost-efficient deployment. These new networking paradigms are sha** not only the network architectures but will also affect the market structure and business case of the stakeholders involved. Due to its capability of splitting the physical network infrastructure into several isolated logical sub-networks, network slicing opens the network resources to vertical segments aiming at providing customized and more efficient end-to-end (E2E) services. While many standardization efforts within the 3GPP body have been made regarding the system architectural and functional features for the implementation of network slicing in 5G networks, techno-economic analysis of this concept is still at a very incipient stage. This paper initiates this techno-economic work by proposing a model that allocates the network cost to the different deployed slices, which can then later be used to price the different E2E services. This allocation is made from a network infrastructure provider perspective. To feed the proposed model with the required inputs, a resource allocation algorithm together with a 5G network function (NF) dimensioning model are also proposed. Results of the different models as well as the cost saving on the core network part resulting from the use of NFV are discussed as well.

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Notes

  1. The authors are aware that these assumptions simplify the modelling but aim to extend the model to more complex cases in a later stage (as will also be described in Sect. 6).

  2. In the current version of the model, we consider that the traffic is static. Yet, in a later stage of the model we will include the dynamicity of the traffic, hence the allocation of the network resources will be dynamic as well (as specified in Sect. 6).

  3. Sensitivity analysis on this factor will be performed in future work.

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Acknowledgements

The authors would like to acknowledge all partners in the SaT5G project, funded by the European Union’s Horizon 2020 research and innovation program under grant Agreement No 761413.

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Authors and Affiliations

  1. IDLab, Ghent University-imec, Ghent, Belgium

    Asma Chiha, Marlies Van der Wee, Didier Colle & Sofie Verbrugge

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  1. Asma Chiha
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Appendix

Appendix

  1. 1.

    Inputs of the cost model of the NFV-based core network:

See Table 14.

Table 14 Cost inputs and calculation for the cost saving model of network virtualization
Full size table
  1. 2.

    Variation of the number of PDU sessions:

We assumed before in modelling the SRR procedure that 3 PDU sessions were active when this procedure is launched, but we do not have a strong assumption on this value. Hence in Fig. 8, we vary the number of PDU sessions to visualize its effect on the required CPU cores for both AMF and SMF using the average frequency without the correction factor. From these results we can deduce, up to until 1 million users, that the number of active PDU sessions does not significantly affect the required CPU cores for AMF and SMF.

Fig. 8
figure 8

Number of CPU cores for AMF and SMF in function of number of users for different number of PDU sessions

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Chiha, A., Van der Wee, M., Colle, D. et al. Network Slicing Cost Allocation Model. J Netw Syst Manage 28, 627–659 (2020). https://doi.org/10.1007/s10922-020-09522-3

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