## 6 Conclusions  
We have addressed the optimization problem of transponder predeployment in dynamic spectrally and spatially flexible optical networks in which the transponder resources are used both for transmitting/receiving (add/drop) spectral super-channels at source/destination nodes and for flexible super-channel regeneration in intermediate nodes which is achieved by means of the B2B regeneration approach. This problem consists in deciding on the number of transponders located in each network node, where the total number of transponders to be deployed in the network is limited. The problem was addressed under the consideration of multiple modulation formats, where each modulation format had specific spectral efficiency and transmission reach, and it was applied in accordance to the length of transparent segments that form translucent (regenerated) lightpaths. We showed that the decision on where the transponders are located has a significant impact on network performance, in particular, on the amount of accepted traffic. We proposed a heuristic method called scaled average used regenerators (SAUR) which makes use of a data analytics approach and decides on the transponder placement by exploiting information about network traffic characteristics and evaluated network performance. To assess the algorithm performance, we performed extensive simulation experiments on two representative network topologies and using several traffic profiles. The main conclusions from this study are the following. Firstly, the SAUR algorithm provided much better performance results (in terms of accepted traffic) in all the evaluated network and traffic scenarios than the reference methods that were implemented and evaluated in this study. It is important to notice that an optimized placement of transponders in an SS-FON, which was achieved with SAUR, allows to increase the network throughput using only the existing resources, i.e., the network operator does not have to invest in new transponder devices or fibers to increase the served traffic and, in consequence, to increase the income. Figure 16: Box graph with placement of transponders given by: (**a**) UNI and (**b**) SAUR algorithms, for the US26 network, TP C, 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. The next conclusion is that the performance gaps between SAUR and the reference methods were larger for the US26 network than for the Euro28 network. It was due to the larger average distance between node pairs in US26 for which optimal placement of transponders is more beneficial since more spectrally efficient modulation formats than BPSK can be used and, therefore, savings in the spectrum usage can be achieved. Moreover, the SAUR method provided the best (highest) utilization of transmission resources (spectrum and transponders). 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. Furthermore, the proposed SAUR method can be used to estimate the number of transponders required in the network to serve a given amount of traffic. In particular, by running the SAUR method assuming different transponders' budgets (number of available transponders), we can obtain the estimated amount of traffic that can be provisioned in the network within a particular transponder budget. Next, by a simple comparison of the obtained values against the predicted network traffic, we can acquire the required number of transponders. This approach is valid for different traffic profiles. In consequence, a better placement of transponders provided by SAUR opens an opportunity to reduce the number of transponders required to serve a particular amount traffic in the network compared to other placement methods.