# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
## 4 Simulation
### Scalability  
To assess the scalability of our proposed approach, we conducted a series of experiments involving larger network topologies and group sizes. We generated larger-scale network topologies through the Waxman model, containing 200, 250, 300, 350, 400, 450, and 500 nodes, respectively. Additionally, we augmented the number of multicast group members to investigate the impact of scalability on our approach.  
Our experiment involved two main scenarios: firstly, maintaining a fixed group size of 20 while increasing the network topology size, and secondly, keeping the topology size constant at 500 while incrementing the group size. We observed the trends in success rates and signaling processing overhead under these varying conditions.  
As shown in Figures 17a and 18a, our results indicate that as topology size and group size increased, the success ratio remained consistently high. The algorithm demonstrated robustness in maintaining efficient multicast routing, highlighting its effectiveness in accommodating a larger population of multicast group members.  
An integral aspect of scalability is the impact on message processing overhead. As depicted in Figure 17b, our experiments demonstrate a manageable and gradual increase in message processing overhead with the growth of the network topology, emphasizing the scalability of our approach. The trend in overhead demonstrates the adaptability of our method to varying scales.  
In Figure 18b, as the group size increases, the processing overhead exhibits a gradual decreasing trend. This observation indicates a favorable scalability aspect of our approach. The manageable reduction in processing overhead with the expansion of the group size underscores the efficiency and scalability of our proposed method. This trend suggests that our algorithm maintains reasonable performance even as the multicast group scales up, showcasing its potential for scalability in handling larger network scenarios.  
In conclusion, the results of the experiments showcase the scalability of the proposed approach. As the network topology increased, we observed a manageable growth in signaling processing overhead, demonstrating that our method scales effectively. Furthermore, the success rate remained stable, indicating that our approach maintains a high level of performance even in scenarios with an expanded network and increased multicast group complexity.  
While our approach demonstrates notable strengths, it comes with certain limitations that merit consideration. Firstly, in comparison to alternative methods, our algorithm incurs higher network costs. This is attributed to our strategy of constraining the search scope for feasible paths to mitigate unnecessary signaling overhead, potentially resulting in locally optimal solutions rather than always achieving the minimum-cost paths. Secondly, our primary focus has been on addressing how to meet application latency requirements during the tree construction phase. However, when network conditions change, the already established multicast routes may no longer satisfy the constraints. Lastly, our algorithm currently emphasizes the latency metric, acknowledging that application Quality of Service (QoS) requirements encompass multiple constraints. It is important to note that future research can explore the algorithm's performance in handling multiple constraint conditions, providing a more comprehensive solution to meet the demands of different application scenarios.