# A Dynamic Restructuring Algorithm Based on Flexible PON Slices
## 4 Results and Analysis  
Considering the application of the actual scenario, this paper conducts a simulation based on the P2P communication model, and chooses the BA scale-free network which is closest to the actual complex network as the basic network model. In this section, the proposed DRA in this paper, DGA in [20], and DONUSA in [21] are simulated and their performance is compared. In view of the clustering characteristics of nodes, the Clustering Coefficient (CC) of the network is set at 0.4 in the generation of network. After the communication relationship between all nodes is determined, Poisson distribution is used to generate the initial traffic in each link. Considering the different arrival rates of users over different time periods, the eigenvalue \(\lambda\) is set at 40 Mbps and 80 Mbps, respectively, to test its impact on the performance of the algorithms. Since the number of ONUs that belong to one OLT depends on the number of PON ports on OLTs and the bandwidth resources, different manufacturers and different loads cause diverse K. So the number of ONUs (N) and the number of OLTs (M) are not the same in different campuses. The simulation takes into account the following factors:  
* DRA is proposed to solve the influence of previous steps on subsequent slicing when the number of OLTs is greater than 2. Therefore, the number of OLTs is set at 8 and 16 to test the influence of the number of OLTs on the performance. * The performance of DONUSA fluctuates when the number of nodes in the initial subgraph is too small [21]. In order to better compare the performance of the three algorithms, the number of nodes (K) in each subgraph is increased from four. * With the expansion of large campuses in the future, the number of ONUs will increase. To verify the effectiveness of the algorithm in the process when the number of ONUs increases, the number of ONUs is set to grow to 512.The growth interval depends on the number of OLTs in different scenarios. Based on the above parameters, the traffic gain of the three algorithms is simulated, and the performance improvement percentage of DRA compared with DONUSA or DGA is calculated. The traffic gain is defined as the reduced value of cross-OLT traffic after running the algorithm compared with the cross-OLT traffic randomly grouped before running the  
Figure 3: The flow of DRAalgorithm, that is, the bandwidth resources saved for the core network. Furthermore, the percentage of performance improvement is calculated as follows (\(Imp_{DGA}\) is used to represent the improved performance of DRA compared to DGA, \(Imp_{DONUSA}\) is used to represent the improved performance of DRA compared to DONUSA, \(TG\) is used to represent the flow gain of the algorithm):  
\[Imp_{DGA}=\frac{TG_{DRA}-TG_{DGA}}{TG_{DGA}} \tag{4}\]  
\[Imp_{DONUSA}=\frac{TG_{DRA}-TG_{DONUSA}}{TG_{DONUSA}} \tag{5}\]  
Figure 4 shows the traffic gain of 3 algorithms with 8 and 16 OLTs, and Figure 5 shows the improved performance of DRA compared to DGA and DONUSA with 8 and 16 OLTs. Hollow nodes are results at \(\lambda\) = 40 Mbs, and the solid nodes are results at \(\lambda\) = 80 Mbs. In Figure 4, the circle nodes represent the traffic gain of DGA, the square nodes represent the traffic gain of DONUSA, and the pentagram nodes represent the traffic gain of DRA. In Figure 5, the circle nodes are the improved performance of DRA compared to DGA, and the pentagram nodes are the improved performance of DRA compared to DONUSA. It can be seen that under the above simulation conditions, DRA has achieved the highest traffic gain. When there are 8 OLTs, the improved performance of DRA compared to DGA and DONUSA are similar, which are roughly between 10% and 30%. When there are 16 OLTs, the performance of DRA is about 30% higher than that of DGA, and about 10% higher than that of DONUSA. You might notice that DRA achieves a much higher  
Figure 4: (a) Traffic gain of three algorithms with 8 OLTs; (b) traffic gain of three algorithms with 16 OLTs.