# Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction
## 5 Performance Evaluation
### Simulation Result and Analysis
Figure 6: Comparison of network node resource variance when different algorithms are used under different resource conditions: (**a**) when resources are sufficient; (**b**) when resources are moderate; (**c**) when resources are scarce. We also compare the total number of network SFC migrations and the SFC service interruption rates when using different algorithms, as shown in Figures 8 and 9. The analysis shows that, across all three resource states, the RP-LBM-PPO algorithm always results in fewer SFC migrations than the RP-LBM-DQN algorithm. Additionally, when resources are sufficient or moderate, the PPO algorithm performs similarly to the DQN algorithm in terms of service interruption rates. This indicates that the PPO algorithm can maintain lower service interruption rates with less migration overhead. Figure 5 shows that resource demand has periodic fluctuations and sudden data surges. Throughout the traffic variations, the RP-LBM algorithm consistently maintains the service interruption rate low. This demonstrates the algorithm's capability to quickly ensure service quality during traffic bursts. By rapidly adjusting resource allocation, the algorithm effectively maintains service continuity. These results further validate the superior performance of the RP-LBM-PPO algorithm in dynamic environments. In contrast, the strategy that does not use pre-migration can only respond passively to changes in resource demand. This approach cannot develop a migration plan in advance and results in the highest service interruption rate. This finding highlights the importance of predicting resource demand and planning ahead to improve network stability and reduce the risk of interruptions. Combined with Figure 7, it can be known that when the network resource variance is small, resource utilization is more balanced. Balanced resource utilization directly impacts the overall performance and efficiency of the network. The PPO and DQN algorithms maintain lower levels of network resource variance through effective load-balancing strategies. In this way, these algorithms achieve more balanced resource allocation and reduce the service interruption rate. However, when resources are scarce, the number of migrations using the DQN algorithm increases significantly. This leads to a sharp rise in the service interruption rate, which is 7.6% higher than that of the PPO algorithm. In a resource-scarce environment, the DQN algorithm tends to frequently adjust network configurations to balance the load. These excessive migration operations increase the system's overhead and complexity, resulting in a higher service interruption rate. The PPO algorithm, on the other hand, uses a more stable policy optimization method. This approach allows the PPO algorithm to adjust policies more effectively under resource constraints, reducing unnecessary migration times. The policy update mechanism of PPO ensures the continuity and stability of network services with only small policy changes. Therefore, in situations where resources are limited, the PPO algorithm demonstrates greater efficiency and stability. It reduces the number of migrations while maintaining a low service interruption rate. This performance indicates that the PPO algorithm has better robustness and adaptability in complex network environments. Figure 7: Comparison of average node resource variance under different resource conditions.