# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
## 4 Simulation
### Network Cost  
Finally, we examined the impact of delay constraints on network cost, as illustrated in Figure 15. Under strict constraints, our algorithm incurs higher network costs compared to others. However, with the relaxation of QoS constraints, our algorithm exhibits a substantial reduction in cost, whereas the cost variations in other algorithms are less pronounced. Combining this outcome with our earlier exploration of success ratios, it can be inferred that the higher costs in our algorithm primarily arise from the addition of links to the multicast tree that satisfy the relaxed delay constraints but come with higher associated network costs, which are not discovered by other algorithms. As the delay constraints are relaxed, paths with higher latency but lower costs become feasible for constructing multicast trees, leading to the observed reduction in network costs for our algorithm.  
Figure 14: Comparison of Message Processing Overhead and Group Size. (**a**) QoS Requirement is 50 ms; (**b**) QoS Requirement is 100 ms.  
We further investigated the trend of network cost as group size changed, with delay constraints set to 50 ms and 100 ms, as depicted in Figure 16a,b. As the QoS constraint delay increased from 50 ms to 100 ms, our algorithm exhibited a significant reduction in cost, with costs gradually decreasing as the group member counts increased. Similar cost trends were observed in other algorithms, showing a gradual decrease. The increase in group size results in more multicast tree nodes distributed throughout the network, making it easier for routing algorithms to select tree nodes closer to the receivers. This contributes to the observed decrease in average cost. This outcome further substantiates the earlier inference. The primary objective of our algorithm is to meet the specified delay constraints. This drives our algorithm to seek paths characterized by lower latency, even if it results in higher costs. Upon the relaxation of constraints, it becomes apparent that paths with lower costs can still adhere to the stipulated delay constraints, resulting in a notable reduction in overall network cost. This further confirms our algorithm's capability to intelligently select the optimal path based on different delay constraints.  
Based on the above test results, we observed that our method exhibits remarkable success ratios compared to other algorithms, particularly in scenarios with stringent delay  
Figure 16: Comparison of Average Cost and Group Size (**a**) QoS Requirement is 50 ms; (**b**) QoS Requirement is 100 ms.  
Figure 15: Comparison of Average Cost and QoS Requirements with a Fixed Group Size of 20 Members.  
constraints. Our approach comes at a higher cost than other algorithms. For instance, when the QoS requirement is set to 50 ms, the cost of our method is 71.8% higher than the average cost of other algorithms. However, our success rate is also 128.4% higher than the average success rate of other algorithms. In relaxed delay constraint scenarios, the cost of our method decreases significantly. This is because, when the search of JOIN message fails to find a path that meets the requirements, we choose lower-latency links to construct the path, resulting in higher costs.  
Additionally, our method keeps the message search scope relatively small, making it superior in terms of message-processing overhead and stability compared to Jia's Algo, which is source-initiated, and QMRP, which blindly expands the search scope while receiver-initiated.