## 5 Results
### Comparison of Algorithms
Furthermore, in Figures 8-10, we show the placement of predeployed transponders in the US26 network yielded by the UNI, COMB, MSU, and SAUR methods assuming three traffic profiles, namely, TP A, TP B, and TP C. Each figure presents the percentage of all available transponders assigned to each node (city) assuming various number of available transponders. The results of the UNI method are pointed out as a red line with value 3.85%, since in this method each of 26 nodes is assigned with the same number of devices. Moreover, we should mention that the presented results obtained by method UNI, COMB, and MSU for a particular traffic profile were the same regardless of the number of available transponders and results of UNI and COMB algorithms were even the same for different traffic profiles. These two facts directly follow from the design of these methods. Only the SAUR method accounts for the number of transponders; therefore, for SAUR we report results obtained for three analyzed values of available transponders denoted as SAUR(10,000), SAUR(15,000), and SAUR(20,000). Analyzing the results, we can observe how the tested methods had different strategies, as they assign the transponders to nodes in the network differently. In particular, the SAUR method tended to place more devices in network nodes in the center of the US (e.g., Dallas, Denver), especially for 20,000 transponders which allows to regenerate longer lightpaths (east coast-west coast traffic) and thus use more efficient modulation formats on shorter segments. In consequence, less transponder and spectrum resources were consumed which, in the end, made it possible to allocate more traffic. The largest differences among the analyzed methods can be observed for the traffic profile A (uniform traffic among all node pairs), while for traffic more dependent on the distance between end nodes (i.e., traffic profiles B and C) the differences among the analyzed methods are smaller. This can be explained by the fact that for traffic profiles B and C, there was more traffic between end nodes located closer and thus less transponders were required for regeneration. Figure 8: Placement of transponders in nodes of network US26 for traffic profile TP A as a function of various algorithms. Figure 9: Placement of transponders in nodes of network US26 for traffic profile TP B as a function of various algorithms. Moreover, it should be noted that SAUR placed much more transponders in some nodes when the budget was 20,000 devices compared to when the budget was 10,000 devices. The largest such differences were observed again for the traffic profile TPA and for nodes located in the center of the US. Once again it can be explained by the fact that with the larger budget of transponders, more regeneration points are possible (see Figures 11-13). These regeneration points are mostly used for longer lightpaths such as the east cost-west coast traffic which allows to change less spectral efficient modulation formats to more efficient modulation formats and thus consume less resources that can be next utilized for other lightpaths. Finally, more traffic can be accepted in the network. Figure 11: Utilization of spectrum and transponders and average number of regeneration points for the US26 network and TP A as a function of various algorithms and number of transponders. Figure 12: Utilization of spectrum and transponders and average number of regeneration points for the US26 network and TP B as a function of various algorithms and number of transponders. Figure 10: Placement of transponders in nodes of network US26 for traffic profile TP C as a function of various algorithms. In Figures 11-13, we report more detailed results for the same cases such as in Figures 8-10. Each figure shows the average results on utilization of spectrum and transponders (bars) and average number of regeneration points (points). The main observation is that in all cases, the SAUR method provided the best (highest) utilization of spectrum and transponders.