## 2 Related Works
For regenerator placement, an iterative method was proposed, where at each iteration a new regenerator site is added to a set of preset regenerator sites obtained in previous iterations, and the model is solved repeatedly for the remaining candidate locations. In Reference [23], a new regenerator allocation algorithm CIRA (circuit invigorating regenerators assignment) was proposed. The CIRA method accounts for quality of transmission (QoT) requirements. In more detail, the algorithm chooses the regenerators to be allocated in the network to reduce the effect of the physical layer and to minimize the QoT blocking. This is achieved by selecting more resistant signal modulation formats, when it is available. In the end, it leads to better overall performance (lower blocking ratio). Four different placement algorithms, namely, MSU, MU, NDF (node degree first), and DA (distance adaptive) were analyzed in the simulations, and the MSU provided the best results. The authors of Reference [24] developed and evaluated an auxiliary-graph-based approach for regenerator assignment in translucent elastic optical networks. The proposed method allows to analyze each possible transparent segment in the considered route. A special cost function was defined for each edge of the auxiliary graph representing a single segment that allows to apply a standard routing algorithm to find a path in the considered auxiliary graph. Several edge cost functions were examined. For regeneration placement, the NDF method was applied. Finally, in Reference [25], the problem of regenerator assignment in dynamic translucent optical networks was examined. It was assumed that each lightpath could be divided into several transparent segments, connected by 3R regenerators capable of accomplishing modulation format and spectrum conversion. It was assumed that the regenerators were placed uniformly in the network, i.e., each node had the same number of regenerators. All the above discussed works are focused on various aspects related to the classic regenerator placement problem. However, we should stress that in this paper--due to the use of the B2B regeneration--instead of the elementary regenerator placement problem, a more broad problem of transponder placement (predeployment) is considered, where the transponders are used flexibly either for signal transmission/reception (add/drop) or signal regeneration purposes. As discussed below in the paper, a proper placement of transponders can significantly increase the amount of network traffic served in the network without any additional capital expenditures, since only the existing resources available in the network are utilized. Therefore, in our opinion, the problem addressed in this paper is of significant importance for network operators. The considered problem is relatively new, and the only paper that addresses the problem of transponders placement in optical networks with B2B regeneration is our work [12], where we presented preliminary research in this area. In particular, in Reference [12], we proposed several simple heuristic algorithms and compared their performance using a US topology. In this work, we continued our research on this problem, namely, we propose a more advanced algorithm based on a data analytics approach and report the results of extensive simulations performed on two representative topologies with deep and insightful discussion of the obtained results. Moreover, we want to mention that we have been working for the last few years on different types of RSA (routing and spectrum allocations) and RSSA problems in EONs and SS-FONs with B2B regeneration (e.g., [8; 11]). However, all our previous papers that concerned B2B regeneration--except Reference [12]--do not address the problem of transponder placement.