## 4 Proposed Strategies for NSR Deployment
### PROMETHEE-II Strategy for NIRA Preparation
As seen in Figure 4b,c, the efficiency rises as the number of nodes grows to 200 and 300, resulting in a maximum efficiency of 0.9 under 10 NSRs for a 300-node physical resource. Additionally, it supports a maximum of 17,554 CPUs and a minimum of 2921 CPUs within the infrastructure of 100 and 300 nodes, respectively. Additionally, a maximum of 16,676 and a minimum of 2775 bandwidth are used under the same operating state. When comparing the performance of the methods, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Definitions** & **Descriptions** & **Range** \\ \hline \(N_{p}\) & The distribution of available security level of a node in real number & (0–1) \\ \hline \(CPU_{\text{best}}\) & The distribution of CPU for each node in unit & U[30; 60] \\ \hline \(BW_{\text{available}}\) & The distribution of bandwidth of each links in unit & U[30; 60] \\ \hline L & The distribution of length of the links & U[1, 5] \\ \hline \(T_{\text{NS}}\) & The total number of NSRs arrived in the time frame & U[5; 35] \\ \hline \(N_{\text{NS}}\) & The distribution of nodes for each NSR & U[10; 30] \\ \hline \(CPU_{\text{NS}}\) & The distribution of CPU requirement & U[5; 25] \\ \hline \(BW_{\text{NS}}\) & The distribution of bandwidth requirement & U[5; 25] \\ \hline \(SPL_{\text{INSR}}\) & The distribution of required security level of a node in real number & (0–0.5) \\ \hline \(LT_{\text{NS}}\) & The time duration of each NSR & T[10; 40] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test case parameters. the suggested approach outperforms the others under a variety of operating situations and with variable NSRs. Among the various methods, the technique based on VIKOR achieves the best results since it employs MCDM-based node ranking for node provisioning and [PERSON]'s shortest route algorithm for link provisioning. In comparison to all other algorithms, the technique based on GLL has the lowest performance. The other algorithms, such as NSR implementation, an SA-based method, and LAVA, all outperformed GLL. When a network is equipped with 100 nodes and requires 30 NSRs, the GLL algorithm records 0.44 as the minimum value for resource efficiency. The suggested technique for the acceptance ratio was analyzed using various values of NSRs (\(T_{NSR}\)) ranging from 5 to 35. The NSRs' lifetimes were chosen at random from the defined range. The findings obtained after the algorithms were successfully executed under various physical infrastructure conditions and are shown in Figure 5.

The data demonstrate that, as the (\(T_{NSR}\)) value is raised, the acceptance ratio decreases even though the number of physical nodes is increased. However, having more nodes in the physical structure results in a much higher acceptance percentage. Additionally, the acceptance ratio increases when the life of the NSRs decreases. The proposed technique achieves a maximum acceptance ratio of 0.99 when ten NSRs are used in conjunction with the 300 accessible nodes. The minimal acceptance ratio is 0.687 when 35 NSRs are used in conjunction with 100 accessible nodes. In contrast to previous algorithms, the suggested approach outperforms them under a variety of operating situations. Among the five algorithms, the technique based on VIKOR achieves the second-best result, which is comparable to the PROMETHEE-II-SLE algorithm's findings. The lowest acceptance ratios are 0.5848 and 0.6069 for 35 NSRs under 100 available nodes, respectively, when network slicing is performed using the LAVA and GLL approaches. Figure 4: Resource efficiency under (**a**) 100, (**b**) 200, and (**c**) 300 nodes in physical infrastructure. Proposed method performance is compared with existing methods such as NSR-NR [19], SA [20], LAVA [21], GLL [22], VIKOR [27]. ### Implementation of the Algorithm under Different Physical Infrastructure Networks

The effectiveness of the proposed algorithm was further validated with different working conditions under two different physical infrastructure networks.