## 5 Performance Evaluation
### Results and Analysis
#### 5.3.1 Performance under the Topology
However, in the semi-central mode environment of Figure 8 and Table 8, although the average throughput of MFLBS has always maintained a relatively high value, its change \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Size** & **MFLBS** & **One-Hop DLBS** & **FCFS** \\ \hline
5 & 46,343,422 & 36,381,752 & 36,381,752 \\
6 & 55,448,964 & 36,797,292 & 36,797,292 \\
7 & 55,700,158 & 36,177,649 & 36,177,649 \\
8 & 47,494,114 & 35,966,003 & 35,966,003 \\
9 & 48,504,502 & 36,400,611 & 36,400,611 \\
10 & 43,418,924 & 35,534,674 & 35,534,674 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average throughput (MB) in FPN model under semi-central pattern. Figure 6: Three algorithms in FPN model under semi-central pattern. in the interval is not obvious. Instead, one-hop DLBS and FCFS display linear growth under the task volume of 15-20 MB. It can be speculated that in this case, in the semi-central mode mesh structure topology, the performance of the MFLBS algorithm reaches a higher bottleneck value very early and is running at such a bottleneck performance. Therefore, the MFLBS algorithm has a large difference in performance for different data transmission modes under a partial mesh topology. In Figures 7 and 8, relative to the average delay, MFLBS has a significant effect on the reduction in the small flow delay. It can be seen that in Figures 7a and 8a, the small flow \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Size** & **MFLBS** & **One-Hop DLBS** & **FCFS** \\ \hline
15 & 139,907,132 & 140,184,725 & 140,184,725 \\
16 & 149,922,066 & 149,046,504 & 149,046,504 \\
17 & 156,089,605 & 153,972,110 & 153,972,110 \\
18 & 168,041,654 & 152,381,859 & 152,109,261 \\
19 & 174,644,435 & 157,415,945 & 157,415,945 \\
20 & 183,855,203 & 154,583,258 & 154,583,258 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average throughput (MB) in mesh topology under uniform pattern. Figure 8: Three algorithms in mesh topology under semi-central pattern. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Size** & **MFLBS** & **One-Hop DLBS** & **FCFS** \\ \hline
15 & 138,298,465 & 122,962,006 & 122,962,006 \\
16 & 143,945,056 & 117,622,948 & 117,258,790 \\
17 & 143,836,667 & 124,642,008 & 124,448,464 \\
18 & 128,924,876 & 115,946,286 & 115,946,286 \\
19 & 135,906,736 & 124,005,139 & 124,005,139 \\
20 & 139,260,103 & 125,773,262 & 125,773,262 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average throughput (MB) in mesh topology under semi-central pattern. Figure 7: Three algorithms in mesh topology under uniform pattern. delays of FCFS and one-hop DLBS are nearly equal under the task size of 20 MB. At this time, compared with the other two algorithms in Figures 7(a) and 8(a), the small flow delay of MFLBS is reduced by nearly 47.5% and 57.8%, respectively. Combined with the above analysis, it can be inferred that the MFLBS algorithm can effectively reduce the small flow delay compared to the one-hop DLBS and FCFS algorithms in any state. The average delay is closely related to its average throughput. We can observe that in Figure 8(b), under the task size of 20 MB, the average delay of the three algorithms is almost the same. It can be analyzed that in the partial mesh topology and semi-central transmission mode, as the transmission tasks gradually increase, the one-hop DLBS and FCFS algorithms gradually reach their bottleneck performance and catch up with the MFLBS algorithm. It is worth noting that, because the flow is evenly distributed in the network under uniform pattern and transmitted on the links within the relative range under semi-central pattern, the FCFS algorithm and the one-hop DLBs have similar performance and trends. At this point, we can understand that one-hop DLBs degenerate into the FCFS algorithm approximately.