# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
## 4 Simulation
### Success Ratio  
Firstly, we analyze the success ratio trends associated with varying delay constraints. The experiments were conducted across 15 independent topologies. Within each topology, 200 groups were generated, each comprising 20 members. The average success ratio was calculated using Equation (10). As depicted in Figure 11, the success ratios of all tested algorithms exhibit an increasing trend as the delay constraints extend from 50 ms to 100 ms. Specifically, our proposed algorithm consistently maintains the highest success ratio. Under stringent delay constraints (QoS Requirement < 100 ms), our algorithm significantly outperforms other algorithms. Particularly under a 50 ms constraint, its performance superiority is most pronounced, surpassing QMRP, PIM-SM, and Jia's Algo by 223.8%, 113.7%, and 86.5%, respectively. This validates the path computation capability of our algorithm, as it endeavors to search for paths in the network that offer smaller delays when constraints are stringent. With the relaxation of delay constraints, Jia's Algo, PIM-SM, and QMRP exhibit similar increasing trends in success ratios. This outcome can be attributed to the fact that as delay constraints become more lenient, routes can be constructed using links that failed under stringent constraints. However, despite the similar growth trends, the success rate and rate of increase of QMRP are notably lower than those of other algorithms. This is due to the blind exploration of feasible paths to the multicast tree during QMRP's join process. Upon discovering that the current path is unsatisfactory, QMRP backtracks and explores alternative paths that may be available. To curb the flooding of join messages in the network, the detection process is terminated after a limited number of attempts, contributing to a reduced success ratio for QMRP. Jia's algorithm initiates a tree search process during the join phase, where it selects an appropriate tree node, and utilizes the underlying routing information to establish a path from the tree node (TN) to the receiver. However, if a path exists between TN and the receiver that satisfies the delay constraint but is not provided by the underlying routing, Jia's algorithm fails to detect it, resulting in a lower success rate compared to our algorithm. Our algorithm addresses the limitations of both approaches by executing a direct tree search within the ICN NRS mechanism and subsequently detecting the path from the tree node to the receiver.  
Subsequently, we analyze the impact of group size on the success ratio under 50 ms and 100 ms delay constraints, as illustrated in Figure 12a,b. With the increase in the group size, our algorithm, PIM-SM, and Jia's Algo exhibit overall stable performance, albeit with minor fluctuations. Our algorithm demonstrates excellent stability, and its performance does not deteriorate with changes in the number of group members. However, the QMRP  
Figure 11: Comparison of Success Ratio and QoS Requirement with a Fixed Group Size of 20 Members.  
algorithm exhibits a significant decline in performance. QMRP stops searching when it reaches a tree node, regardless of whether it finds a suitable path. Therefore, as the number of group members increases, the number of tree nodes also increases, leading to a higher probability of the algorithm stopping prematurely and resulting in a decrease in success rate.