## 5 Performance Evaluation
### Results and Discussion
We can observe that there is a small performance difference between DDQN-SP and the proposed MRL-SFCP algorithm, since they use DRL to place VNF instances and the IoT-SRs are be rejected when there is no path for route traffic or the selected path has insufficient resources. The results demonstrate the consistent superiority of the acceptance rate of IoT-SRs achieved using the proposed MRL-SFCP algorithm in comparison to the other algorithms under investigation. The average reward of IoT-SRs in the SWN topology is illustrated in Figure 11 as the number of IoT-SRs varies from 20 to 200. It can be observed that the MRL-SFCP algorithm attains the highest reward after 80 IoT-SRs. This phenomenon can be attributed to the consistent identification of the shortest path by DDQN-SP, rendering it well-suited for scenarios featuring a limited number of IoT-SRs. Nonetheless, as the number of IoT-SRs escalates, the DDQN-SP struggles to maintain a balanced utilization of network resources. This causes a rapid decline in the average reward after 80 IoT-SRs. The three other compared methods exhibit similar phenomena after 80 IoT-SRs. Based on the results obtained in the SWN, the proposed MRL-SFCP algorithm exhibits superior performance. Figure 12 illustrates the acceptance rate of IoT-SRs within the SFN topology, showing that both our proposed MRL-SFCP and DDQN-SP obtain the highest traffic acceptance rate of IoT-SRs in comparison to other methods. With the number of IoT-SRs increasing,  
Figure 11: Average reward of IoT-SRs in the SWN. Figure 10: The acceptance rate of IoT-SRs in the SWN. the acceptance rate of IoT-SRs shows a significant decrease across all investigated algorithms. This decline is attributed to the increased resource consumption costs and the emergence of bottleneck links and cloudlets. Furthermore, we can observe that the result for all algorithms exhibits the same trend as the aforementioned network. The average reward of IoT-SRs is presented in Figure 13 for the SFN topology, considering a range of 20 to 200 IoT-SRs. It can be observed that all the methods exhibit a similar changing trend in terms of average reward, aligning with the aforementioned network characteristics. We can observe that our proposed MRL-SFCP algorithm achieves greater rewards than the other methods for a large number of IoT-SRs and the best traffic acceptance rate of IoT-SRs in most cases. In this work, the resource demands of IoT-SRs are constantly changing, which results in a dynamic scenario. Hence, the incorporation of dynamic SFC placement strategies becomes essential in minimizing the weighted sum of resource utilization cost and end-to-end latency. The outcomes highlight the adaptability to network changes and indicate the efficiency of resource utilization of the proposed MRL-SFCP algorithm. In view of the experimental analyses under different network topologies presented above, we only conduct an experiment on the SFN topology to compare the accepted traffic throughput. Table 2 shows the throughput corresponding with 100, 150, and 200 IoT-SRs. When the number of IoT-SRs is 150, we can see that the throughput of our proposed MRL-SFCP algorithm is about 1 Gbp higher than that of DDQN-SP, 1.6 Gbps higher than that of DLA-DVPRP, 2 Gbps higher than that of DLG, and 4.7 Gbps higher than that of the  
Figure 12: Success acceptance rate of IoT-SRs in the SFN. Figure 13: Average reward of IoT-SRs in the SFN. random method because DDQN-SP, DLG, and the random method cannot ensure link load balancing. Although the DLA-DVPRP algorithm takes into account load balancing for links and nodes, its performance is not as good as that of MRL-SFCP and DDQN-SP, which consider both link delay and link jitter when searching for routing paths for IoT-SR. In order to present the computational efficiency of these algorithms, we compare them in terms of running times. Table 3 illustrates that MRL-SFCP has a significantly lower running time in comparison with other algorithms.