## 5 Performance Evaluation
### Results and Discussion  
We first use the trained VNF placing network to choose cloudlets for arrived IoT-SRs online. In order to evaluate the performance of the VNF placing network, simulations are carried out across three distinct network topologies, specifically RN, SWN, and SFN. In this experiment, we mainly compare the performance of three types of algorithms, namely machine learning algorithm DDQN, a greedy algorithm, and a random algorithm. When required VNFs of IoT-SR are not in the chosen cloudlets and there are insufficient resources to place VNF instances, we label the IoT-SR as \"cannot be accepted\" and set the reward as \(-200\) directly. The simulation result is obtained by randomly setting CPU and bandwidth resources, and the average value of twenty experiments is taken into consideration. The success acceptance rate of IoT-SRs in various topologies is illustrated in Figure 6. It is evident from the results that the DDQN algorithm consistently attains the highest traffic acceptance rate. The the average acceptance rate of IoT-SRs using the DDQN algorithm across the three networks is 18.4% higher than that of the random method and 20.8% higher than that of the DLG. Figure 7 presents the average rewards of IoT-SRs under three networks. The simulation results demonstrate that under all network topologies, our proposed DRL-based method outperforms the other approaches by a significant margin. In most cases, the performance of the greedy method is inferior to that of the random method, as it always chooses the cloudlets with the lowest delay, easily making the resources of some cloudlets become very small after a number of IoT-SRs, while the random method selects all cloudlets with equal probabilities for each IoT-SR.  
Figure 6: The acceptance rate in different network topologies. Figure 7: The average reward of IoT-SRs in different network topologies. In Figure 8, an evaluation is conducted to compare the traffic acceptance rate within the RN topology. The IoT-SRs arrive at IoT-MEC networks randomly, and the required resources can be acquired only when they come into the network. The results indicate that in the majority of cases, MRL-SFCP exhibits superior performance compared to the other approaches. This finding signifies that the trained agent effectively guides the SDN controller in dynamically placing VNFs and identifying appropriate route paths. Additionally, with an increase in the number of IoT-SRs, as the DLG and random methods cannot dynamically place VNFs and they may fail to discover paths capable of serving IoT-SRs, they achieve poor performance. The DLA-DVPRP uses heuristic algorithms for path selection, achieving lower performance than MRL-SFCP and DDQN-SP. However, since the DLA-DVPRP can dynamically place VNFs, its performance is higher than that of the DLG and random methods. To facilitate a comprehensive understanding of the experimental findings, Figure 9 shows the mean reward of IoT-SRs in the RN. The defined reward in this study is comprised of the weighted summation of resource utilization cost and delay, aligning with our optimization goals. It is evident that the DDQN-SP surpasses the compared approaches solely when the count of IoT-SRs is less than 35. When the number of IoT-SRs is greater than 40, a noticeable and rapid decline is observed. Conversely, the proposed MRL-SFCP algorithm demonstrates minimal variation and achieves the highest reward. Based on these observations, it can be inferred that the performance of the MRL-SFCP algorithm surpasses that of other methods within the RN topology. Figure 8: The acceptance rate of IoT-SRs of the RN. Figure 9: The mean reward of IoT-SRs within the RN. In this study, we compare the acceptance rate of IoT-SRs obtained through the MRL-SFCP approach with that obtained using alternative methods. Figure 10 presents the traffic acceptance rate of different algorithms from the SWN topology while varying the number of IoT-SRs from 20 to 200.