## 5 Performance Evaluation
### Results and Analysis  
#### 5.3.1 Performance under the Topology  
In the tree topology FPN, we use 1 MB as the step size in the uniform and semi-central modes, and we increase the task of sending data from 5 MBps to 10 MBps. Combining the average throughput of each algorithm under different task volumes in the FPN model in Figure 5c and Table 5, we can analyze: at the beginning, the network under uniform pattern can fully accommodate the flow of 5-7 MB tasks, so the average throughput of these three algorithms is almost the same; but, with the increase in network congestion, one-hop DLBS cannot effectively reduce the congestion despite re-selecting the path for the flow. Therefore, it degenerates to the FCFS algorithm. As the one-hop DLBs and FCFS networks reach the bottleneck, the rise of average throughput stops. When the MFLBS algorithm has a size of 10 MB at the sending end, the average throughput has increased by about 9.8% compared to the 9 MB data flow sent. Under the semi-central pattern in Figure 2, the average throughput of one-hop DLBS and FCFS maintains a relatively stable value under the task volume of 5-10 MB as shown in Figure 6c and Table 6. It can be speculated that the performance of network scheduling at this time has reached the bottleneck. In contrast, although MFLBS has a trend of first increasing and then decreasing with the increase in the task volume, its value always maintained a relatively high throughput. Analyzing the reason, the average throughput of MFLBS improves with the increase in the total workload of the network under the task volume of 5-7 MB. As the task volume continues to increase to 10 MB, the link congestion also intensifies, so the average throughput declines. Therefore, the MFLBS algorithm has better adaptability and tolerance to the consequent increase in network tasks. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Time slot & 1 (s) \\ Link bandwidth & 15 (MBps) \\ Duration of flow generation & 500 (s) \\ The number of new generating flows per time slot & 10 \\ The number of pings per time slot & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters. Figure 5: Three algorithms in FPN model under uniform pattern. In the uniform mode of the FTP model in Figure 1, the data flow delay in MLFLBS is not sensitive to network congestion, and its growth rate, as the amount of data flow tasks increases, is significantly lower than the one-hop DLBS algorithm and the FCFS algorithm. Like the FCFS algorithm from 5 MB to 10 MB, the average delay increased by 3620.7%, while the MFLBS algorithm only increased by 209.5%. Compared with the delay of one-hop DLBS and FCFS under uniform mode, which increases approximately exponentially, the delay of one-hop DLBS and FCFS under semi-central pattern increases linearly in Figure 6a,b. Although the delay of MFLBS under the semi-central pattern is less than that of the other two algorithms, its average delay increases by 31,554.6% under the task load from 5 MB to 10 MB, which shows that its performance is inferior to the performance under uniform pattern, since under the semi-central pattern, more flows are restricted to a relative range of links for transmission. Therefore, in the static routing network, more flows are allocated bandwidth equally, and each flow will obtain less bandwidth, thereby increasing the delay and shortening the average throughput of the entire network. Similarly, in the partial mesh topology, we repeat the above experiment in uniform and semi-central modes. But to adapt to the mesh topology with better connectivity, we use 1 MB as the step size this time, increasing from 15 MBps to 20 MBps to send data task. For the average throughput, we can observe from Figure 7c and Table 7: one-hop DLBS and FCFS maintain a relatively stable value in the task change interval from 15 MB to 20 MB, while the average throughput of the MFLBS algorithm is significant in the interval increase.