# A Heuristic Fuzzy Based 5G Network Orchestration Framework for Dynamic Virtual Network Embedding
## 5 Simulation Evaluation
### Simulation Results
#### 5.2.3 Discussion  
This section demonstrates the effectiveness of the proposed algorithm by comparing the obtained results with the literature, specifically NNR [15], CN [16], and VIKOR [17]. It is presumable that the dynamic provisioning service for network requests is made available to all of the proposed algorithms. When the PI is outfitted with 300 nodes and is responsible for catering to a wide variety of network requests, resource efficiency and acceptance rate, are taken into consideration for the comparison. The performance of the various algorithms is illustrated in Figure 9, which is organized according to a variety of categories.  
Figure 8: Resource utilization of DVNE. (**a**) CPU used, (**b**) bandwidth used.  
Figure 6: Resource efficiency of dynamic provisioning: (**a**) 100 nodes, (**b**) 200 nodes, (**c**) 300 nodes.  
Figure 7: Acceptance ratio of dynamic provisioning: (**a**) 100 nodes, (**b**) 200 nodes, (**c**) 300 Nodes.  
It is clear from Figure 9(a) that the CN algorithm has the lowest throughput of any and all of the other algorithms when they are combined. VIKOR NNR and DVNE all outperformed CN in terms of performance. When there are 300 nodes and 30 NSRs, an efficiency of 0.753 is recorded. The proposed method for calculating the acceptance ratio is evaluated using NRs in the range of 5 to 35. The life-spans of the NRs are picked at random from a range that has been determined in advance. The results obtained after the algorithms have been successfully run under the conditions of a physical infrastructure consisting of 300 nodes are displayed in Figure 9(b). The acceptance ratio drops when the total number of NR points is increased. On the other hand, when NR lifespans are decreased, acceptance ratios tend to increase along with them. A maximum acceptance ratio of 0.98 can be accomplished with the proposed method, while a minimum acceptance ratio of 0.7 can be accomplished with 35 NRs. The NNR algorithm is ranked in second place because it produces results that are on par with those produced by the DVNE algorithm. When it comes to allocation of network resources using the CN and VIKOR approaches, the lowest acceptance ratios are 0.68 and 0.66 for 35 NRs under available nodes, respectively. These numbers are based on the number of nodes.  
To further validate the performance of the proposed algorithm, in addition to resource efficiency and acceptance ratio, the time required by each of the various algorithms to finish the task is analyzed and compared. As can be seen in Figure 10, a comparison of the execution times for provisioning while using various NRs is performed. The amount of time required to carry out the algorithmic procedures grows in direct proportion to the total number of NRs and available nodes in the physical infrastructure. The time needed to complete the resource allocation algorithm developed by DVNE is significantly greater than that required by the other two algorithms under any and all conditions of the operation.  
Figure 10: Execution time of DVNE with other algorithms.  
Figure 9: Performance results. (a) Resource efficiency, (b) acceptance ratio.  
When a physical infrastructure consists of 100 nodes, it takes fewer than 10 milliseconds to serve NRs with values ranging from 5 to 35. In addition, the same number of NRs served on the PI with 300 nodes requires a response time that is only slightly longer than 10 milliseconds. When compared with the SA approach's execution time, the VIKOR approach's execution time is significantly faster. The amount of extra time needed to finish a task when using the SA approach can range anywhere from 100 ms to 1000 ms.