# A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method
## 4 Simulation
### MFT Congestion Comparison  
The level of MFT congestion is another important indicator for measuring the effectiveness of MJN selection methods. A reasonable MJN selection method can effectively reduce MFT congestion, avoid single-point performance bottlenecks, and improve multicast scalability.  
In Figure 7, we have illustrated the level of MFT congestion for various algorithms. We use max MFT entry number to measure MFT congestion. The max MFT entry number refers to the number of multicast forwarding states carried by the most heavily loaded node in the entire topology, measured in units of \"count\".  
Figure 6: Traffic gap comparison.  
Figure 7: MFT Congestion comparison.  
Compared to the traffic load, it is more challenging to balance multicast forwarding states. This is because the forwarding state is maintained on the node, and a node can have multiple links. Once a node is in a multicast tree, its forwarding state increases by one. However, only one to two links in all of the node's links will have an increase in load. Therefore, the granularity of adjustment for link states are smaller than that of the forwarding states. This results in WAVE or RSPT having a more severe congestion level for forwarding states than for traffic load. Specifically, the congestion level for forwarding states in WAVE and RSPT can reach about 40% of that achieved by SPT, while the congestion level for the traffic load is only about 30% of that achieved by SPT.  
The curve of MFT congestion for different algorithms is similar to that of link stress congestion. The performance difference between RSPT and WAVE is within 12%, indicating that RSPT can effectively balance a forwarding state load. Although Near can join the multicast tree using the shortest path, its performance is slightly inferior to RSPT and WAVE due to its inability to avoid high-load nodes. Due to building long paths, SPT can increase the workload of more nodes; At the same time, SPT cannot perceive the network load status. Therefore, SPT has the worst performance.  
To demonstrate the impact of topology size on the performance of RSPT and WAVE, we compare the relative performance gap between the two on topologies of different sizes. Results are shown in Figure 8. To illustrate the average difference between RSPT and WAVE, we defined the MFT gap index, which can be expressed by Formula (4).  
\[MFTGap=\frac{MT_{RSPT}-MT_{WAVE}}{MT_{WAVE}} \tag{4}\]  
_MT_ represents the max MFT entry number of the network. MFTGap refers to the difference in forwarding state congestion level between RSPT and WAVE. As the forwarding state congestion level of RSPT has always been higher than that of WAVE, this index is greater than 0. The larger the value of this index, the more serious the forwarding state congestion of RSPT compared to WAVE, and the worse the performance relative to WAVE. The smaller the value, the more similar the forwarding state congestion level between RSPT and WAVE, and the closer their performance.  
The conclusion we obtained from Figure 8 is similar to that from Figure 6. The difference lies in that, from an overall perspective, the MFTGap index is around 12%, which is 5% lower than the TrafficGap index. This is because achieving balance in the forwarding state is difficult for both WAVE and RSPT, which we have discussed before. Therefore, the difference between the two is not as significant as when balancing the traffic load.  
Figure 8: MFT gap comparison.