## 4 Proposed Strategies for NSR Deployment
### PROMETHEE-II Strategy for NIRA Preparation
The execution is carried out individually in the SFN and SWN network settings, with three node levels (100, 200, and 300) in each network configuration. Table 4 displays the outcomes that are achieved. From the table, it is evident that more CPU units are serviced and that more bandwidth is consumed when the network is organized according to the SWN topology. For 30 NSRs with 300 accessible nodes, an SWN-based network records the maximum values of CPU serviced and bandwidth consumed as 17,554 and 16,676, respectively, for the maximum values of CPU serviced and bandwidth consumed for the same circumstance are recorded by the SFN as 13,275 and 12,611, respectively. The analysis was furthered by running the algorithm under three different numbers of NSRs with fixed lifetimes of the requests, with \(LT_{min}=10\) and \(LT_{max}=40\) as the starting

\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{8}{c}{**100 Nodes**} & \multicolumn{8}{c}{**200 Nodes**} & \multicolumn{8}{c}{**300 Nodes**} \\ \cline{2-13}  & \multicolumn{4}{c}{**BW Utilized**} & \multicolumn{2}{c}{**CPU Served**} & \multicolumn{2}{c}{**BW Utilized**} & \multicolumn{2}{c}{**CPU Served**} & \multicolumn{2}{c}{**BW Utilized**} & \multicolumn{2}{c}{**CPU Served**} \\ \hline  & **NSRs** & **Min** & **Max** & **Min** & **Max** & **Min** & **Max** & **Min** & **Max** & **Min** & **Max** & **Min** & **Max** \\ \hline \multirow{3}{*}{SFN} & 10 & 23



and ending values. As demonstrated in Table 5, the resulting values of the CPU served and bandwidth consumed were recorded for NSRs, which ranged between 10 and 30 as shown in the table. It is obvious from the table that both forms of the physical structure record the maximum values of the CPU served and bandwidth consumed for LTmin requests, and vice versa for LTmax requests, and that this is true for both types of physical structure. The SWN structure, on the other hand, is able to share more resources than the SFN structure, as seen by the highest recorded value of 16,644 CPU served and 17,625 bandwidth consumed for 30 NSRs on the provided structure, which has 300 nodes in total. The SFN, on the other hand, could only serve 9322 CPUs while consuming just a 983 bandwidth for the same number of physical nodes. Furthermore, it is shown that the SWN may deliver superior services when compared to the SFN structure, and that service providers can earn a higher profit while simultaneously enhancing the utility. ## 6 Conclusions

The solution approach for effective network slicing in a 5G mobile network environment was developed in this study. The physical infrastructure, NSRs, and deployment plan were all modelled. To achieve a slicing-friendly infrastructure, physical infrastructure was represented as a small-world network. Physical infrastructure and NSR factors were found for efficient resource distribution. The deployment technique made use of PROMETHE-II for node allocation and SLE for node-to-node connection creation. For the network slicing performance assessment, the resource efficiency and acceptance ratio were taken into account. The suggested algorithm's performance was investigated under various network operating circumstances and varying NSR parameter values. The suggested algorithm's competence was shown by comparing the result to other algorithms in the literature. Furthermore, to demonstrate the superiority of the small-world network (SWN), the suggested method was tested in a scale-free network (SFN) and a comparative analysis was performed. The findings reveal that the suggested algorithm operates better under various operating situations, and the small-world network provided a much better slicing provision. It is possible to develop this work to include the likelihood of the nodes' placement, as the user cannot stand motionless. Machine and deep-learning-based node provisioning may also be advised for quick deployment. Conceptualization, S.V., T.S. and H.-G.J. ; methodology, S.V. and T.S. ; investigation, S.V. and T.S.