## 5 Experiment
### Experimental Results and Analysis  
In order to verify the performance of MAFS, this paper compares it with the ECMP algorithm and traffic scheduling schemes based on DDPG and MADDPG through an experimental simulation and analyzes the experimental results under different performance indexes. To analyze the differences between the different schemes, the link bandwidths are adjusted to 100 Mbps, 500 Mbps, 1000 Mbps, and 2000 Mbps, respectively. The experimental results indicate that under different bandwidth settings, the average throughput of MAFS is higher than that of the ECMP, DDPG, and MADDPG traffic scheduling schemes, while the average round-trip delay, packet loss rate, and flow completion time of MAFS are lower than the other three comparison methods. The average throughput is used to evaluate network performance, which represents the capability of the traffic in the network as it is forwarded along paths. As depicted in Figure 4, the comparison of the average throughput shows that compared to the ECMP, DDPG, and MADDPG methods, the MAFS method increases the average throughput by 54.19%, 24.79%, and 16.93% when the bandwidth is set to 100 Mbps, respectively. These results indicate that the proposed MAFS method can update routing strategies through \begin{table}
\begin{tabular}{l l} \hline \hline
**Simulation Tool** & **Description** \\ \hline Operating System & Ubuntu 18.04 64-bit \\ \hline Network Simulation Platform & Mininet 2.2.2 \\ \hline Virtual Switch & Bmv2 Software Switch \\ \hline Programming Languages & Python, P4 \\ \hline Traffic Generation Tool & iperf \\ \hline Python Management Software & Anaconda \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptions of simulation experiment environment. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter Name** & **Value** & **Description** \\ \hline BATCH\_SIZE & 1024 & Size of mini-batch sampling \\ \hline MEMORY\_CAPACTITY & 10,000 & Capacity of buffer pool \\ \hline MAX\_EPISODE & 500 & Maximum number of episodes \\ \hline MAX\_STEP & 25 & Maximum number of steps per episode \\ \hline LR\_A & 0.01 & Actor network learning rate \\ \hline LR\_C & 0.02 & Critic network learning rate \\ \hline GAMMA & 0.99 & Discount factor \\ \hline TAU & 0.01 & Target network hyperparameter \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Network model parameter values of MAFS. continuous learning historical experience and training, selecting the optimal forwarding path for the traffic, thereby achieving network load balancing. The comparison of the average round-trip delay is shown in Figure 5. When the bandwidth is set to 1000 Mbps, the average round-trip delay of the MAFS method is reduced by 33.30%, 24.27%, and 19.70%, respectively, compared with the ECMP, DDPG, and MADDPG methods. These results demonstrate that the MAFS method can effectively reduce the average round-trip delay, improve the traffic transmission efficiency, and alleviate link congestion. The comparison of the packet loss rate is shown in Figure 6. Under different bandwidth settings, the MAFS method can effectively control the packet loss rate within a small range. Compared with ECMP, DDPG, and MADDPG, the loss rate of the MAFS method is kept below 15.54%, which can intelligently allocate forwarding paths for traffic, alleviate link loads to a certain extent, effectively reduce the packet loss rate, and ensure the effective transmission of packets. Figure 4: Average network throughput. Figure 5: Average round-trip delay. As shown in Figure 7, with different bandwidth settings, the MAFS method can effectively reduce the flow completion time. Particularly, when the bandwidth is set to 100 Mbps, the flow completion time of the MAFS method is reduced by 36.84%, 26.47%, and 13.46% compared with the ECMP, DDPG, and MADDPG methods, respectively. The results demonstrate that the MAFS method can achieve faster data transmission, thereby making more efficient use of the available bandwidth to improve network utilization.