# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 5 Simulation Results and Discussion
### SIRA Scheme  
This phase focuses on the allocation of resources for slice isolation, as shown in Figure 3. This system is evaluated by considering clusters of 100, 200, and 300 NSR nodes in different scenarios with 10, 20, and 30 NSR nodes, respectively. Table 2 shows the range of values from which network features are randomly selected. In order to better measure performance, the lengths of the graph's edges are fixed at one unit each. Node-generated values are classified using the Random Forest technique and made available as slices in PI and NSR to allocate resources. By assigning the available resources of PI slices to NSR slices in an appropriate manner, the SIRA scheme guarantees that the required NSRs will have access to the necessary nodes and links. The obtained resource efficiency under different system operating conditions is shown in Figure 6. Figure 6 summarises this generalisation about NSRs and resource efficiency by demonstrating how an increase in the number of NSRs reduces the network's resource efficiency while an increase in the number of nodes in the physical infrastructure raises efficiency. The resource efficiency calculation shows that adding more nodes to a network makes the shortest way shorter, which makes better use of the resources that are available. Additionally, more powerful and numerous nodes should be made available to accommodate the growing number of NSRs. Using the suggested method on an infrastructure that includes 100 nodes, efficiencies of 0.759, 0.746, and 0.733 are achieved for NSRs of 10, 20, and 30, respectively, as shown in Figure 7. The number of PI nodes increases to 200 and 300, and efficiency with that resource rises as well, achieving a maximum of 0.79 under 5 NSRs; a total amount of 9429 CPUs are utilised. Likewise, in the same operational state, bandwidth utilisation varies between a high of 29,662 and a low of 4300. Comparisons are made between the proposed algorithm's performance and that of other algorithms in the literature, including those from the CN [24], SA [42], LAVA [43], GLL [44], VIKOR [27]. The algorithms are run under similar operating conditions to facilitate comparison. Figure 7 depicts the algorithms' resource efficiency results. SIRA outperforms all other resource allocation methods in a wide range of operating conditions and NSRs, as seen in Figure 7.  
When compared to the MCDM-based node ranking algorithm, the VIKOR-CNSP method of resource allocation provides significantly better performance. The performance of NSR implementation, the SA-based approach, and LAVA were all superior to that of GLL. An efficiency of 0.753 is observed for 300 PI nodes and 30 NSRs. The suggested method for figuring out the acceptance rate is tested with NSRs ranging from 5 to 35. The expected lifetimes of the NSRs are arbitrarily chosen. The outcomes of the algorithms' effective execution across a range of physical infrastructure circumstances are depicted in Figure 8. Even though there are more actual nodes, the acceptance ratio drops as the overall NSR rises. Figure 6: Resource efficiency for SIRA. Because more nodes are present, a greater percentage of the population accepts it. As NSR lifespans are shortened, the acceptance ratio also rises. The maximum acceptance ratio that can be attained with the suggested method is 0.98 when just 10 NSRs are used and 300 PI nodes are provided. A minimal acceptance ratio of 0.84 is achieved when 35 NSRs and 100 accessible nodes are used. It has been shown that the proposed algorithm outperforms previous approaches under numerous operational conditions. VIKOR-CNSP is the second-best algorithm, with results that are comparable to those of the SIRA algorithm. For 35 NSRs under available nodes, the lowest acceptance ratios while employing the LAVA and GLL techniques to allocate network resources are 0.48 and 0.60, respectively. Algorithms are evaluated not only by their resource efficiency and acceptance rate but also by how quickly they can be executed. This determines how quickly each algorithm can complete a task.