## 5 Results
### Comparison of Algorithms  
The next goal of experiments was to compare the novel SAUR transponder placement algorithm against reference methods described in Section 4 and adapted to the considered problem, namely, UNI [18], ND [18], RO [18], CONF [12], COMB [12], and MSU [17]. In Table 3, we show the ranking of the tested methods for US26 and Euro28 networks in terms of the accepted traffic. Three values of the numbers of available transponders were considered: 10,000, 15,000, and 20,000. The same traffic profiles as in previous sections were analyzed. For each separate case (i.e., network topology and traffic \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of TRXs**} & \multicolumn{4}{c}{**US26 Network**} & \multicolumn{4}{c}{**Euro28 Network**} \\  & **TP A** & **TP B** & **TP C** & **TP D** & **TP E** & **TP A** & **TP B** & **TP C** & **TP D** & **TP E** \\ \hline
10,000 & 1.4 & 1.4 & 1.0 & 1.2 & 1.0 & 1.4 & 1.4 & 1.0 & 1.0 & 0.8 \\
15,000 & 1.6 & 1.4 & 1.2 & 1.2 & 1.0 & 2.0 & 1.4 & 1.0 & 1.2 & 1.2 \\
20,000 & 2 & 1.4 & 1.0 & 1.4 & 0.8 & 2.0 & 1.8 & 1.4 & 1.6 & 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Tuning of the SAUR algorithm—values of the scaling parameter yielding the best result as a function of different number of transponders (TRXs) and various traffic profiles (TP)  
Figure 6: Tuning of the SAUR algorithm—accepted traffic as a function of tuning parameter \(\beta\) for the US26 network with different traffic profiles: (**a**) 10,000 transponders, (**b**) 20,000 transponders. Figure 7: Tuning of the SAUR algorithm—accepted traffic as a function of tuning parameter \(\beta\) for the Euro28 network with different traffic profiles: (**a**) 10,000 transponders, (**b**) 20,000 transponders. profile), the transponders were predeployed using a particular method. Next, we ran the dynamic routing ARBR algorithm to measure the amount of accepted traffic assuming the 1% of BBP. The best predeployment method received 1 point (first place), the second method received 2 points (second place), etc. Next, we averaged the ranking over all five tested traffic profiles for a particular number of transponders. We can easily notice that the SAUR method always provided the best results. The MSU, UNI, and COMB methods share places 2-4, and the position of each method depends on the analyzed scenarios (topology and number of transponders). The three remaining methods (ND, RO, and CONF) provided much worse results compared to the other methods. Therefore, for further detailed analysis, we used the four best algorithms, namely, UNI, COMB, MSU, and SAUR. In Tables 4 and 5, we report detailed results obtained for UNI, COMB, MSU, and SAUR for US26 and Euro28 networks, respectively. We present two performance metrics: accepted traffic in NTUs and the percentage distance to the SAUR method. First of all, the results confirm the trends described above, in particular, in all cases the SAUR method is the best one, i.e., it allows to provision in the network the highest amount of traffic among all tested approaches. However, we can easily notice that the differences between examined methods strongly depend on the number of transponders and selected traffic profiles. In particular, with the increase of the available transponders, the gaps between SAUR and MSU decrease regardless of the traffic profile. In the case of UNI and COMB methods, this trend is not so regular. But--comparing the performance for different number of transponders--the smallest gaps between the analyzed methods are obtained for the largest number of 20,000 transponders. This trend can be explained by the fact that with larger number of transponders the placement of transponders effects the results (accepted traffic) to a smaller extent, since more transponders cause that the blocking occurs mostly due to the limited resources of optical spectrum and additional transponders (regardless of their placement) cannot help overcoming this problem.