## 5 Results
### Comparison of Algorithms
In other words, the placement of transponders yielded by SAUR allowed to utilize the network resources to the highest extent which, in consequence, allowed to allocate more traffic than the other methods. In terms of the number of regeneration points (i.e., how many times on average the lightpath was regenerated), the SAUR and MSU methods experienced the highest values. Finally, both traffic profile and number of available transponders influenced the observed results; however, the impact was not very large. We want to point out that the applied dynamic routing algorithm ARBR [11] used 5 candidate routing paths in the routing process and was able to efficiently adapt to the limited spectrum and transponder resources available in the network. In consequence, even when the transponders were not predeployed in an optimal way in the network, the algorithm itself can to a large extent improve the results of accepted traffic by adapting the routing and spectrum allocation process. However, even with such an intelligent dynamic routing method, the SAUR algorithm outperformed the other analyzed algorithms. Note that due to the limited space, we did not present detailed results of transponder placement for the Euro28 network; however, the obtained trends were comparable to those in the US26 network. The last Figures 14-16 show more detailed results of two methods: UNI and SAUR for US26 network, 15,000 transponders, and traffic profiles: TP A, TP B, and TP C. Each graph shows the box plot of used transponders. The data were collected in every time iteration of the dynamic routing simulations. The dotted line shows the number of transponders predeployed in each node according to the results yielded by a particular algorithm. For the UNI method this value was constant (i.e., each node was assigned with the same number of transponders), while for the SAUR method, it was different for particular nodes. The red line represents the average observed values of transponder usage during the simulations (i.e., how many transponders were used for transmitting/receiving of optical signals at the source/destination nodes and regeneration of the signals at some intermediate nodes). In turn, the boxes show data on transponders' average usage through their quartiles. The presented figures clearly confirm that the SAUR method can utilize transponders much better than UNI. Moreover, the placement of transponders yielded by the SAUR method (limit of transponders) allowed more sustainable use in particular nodes, while for the UNI method some nodes used only a small part of available devices. Figure 13: Utilization of spectrum and transponders and average number of regeneration points for the US26 network and TP C as a function of various algorithms and number of transponders. Figure 14: Box graph with placement of transponders given by: (**a**) UNI and (**b**) SAUR algorithms, for the US26 network, TP A, and 15,000 transponders. The dotted line shows the number of transponders in each node according to a particular algorithm. The red line represents the average observed values of transponder usage, while the boxes show data through their quartiles. Figure 15: Box graph with placement of transponders given by: (**a**) UNI and (**b**) SAUR algorithms, for the US26 network, TP B, and 15,000 transponders. The dotted line shows the number of transponders in each node according to a particular algorithm. The red line represents the average observed values of transponder usage, while the boxes show data through their quartiles.