## 4 Network Layer: The Main Building Block of Fixed Networks
### Five Generations of Backbone Networks: From PDH to OTN/SDN
#### 4.2.2 Machine Learning  
Machine Learning can aid in fully exploiting all physical layers and resource allocation innovative technologies, which will bear fruit in emerging solutions and services, as they can fine-tune the system performance and calculate metrics employed to increase the smartness (toward intelligence) of the end-to-end system on a network-layer basis [76, 77]. In addition, ML can aid in improving the overall accuracy of performance estimations. More specifically, the ML-based quality of transmission (QoT) estimation problem has received a lot of attention in the recent literature [78, 79, 80]. Its aim is to aid in ensuring adequate QoT for all network services during their whole service life cycle. Moreover, ML methods can be used in combination with analytical expressions in order to increase the accuracy in cases where analytical expressions are inaccurate, e.g., when the accumulated dispersion is low and the Gaussian noise assumption for the fiber nonlinearity is erroneous [80]. As a consequence, the higher accuracy can directly lead to decreased operational margins, as analyzed in the previous section in the context of IA-RWA, which are set to \"compensate\" the modeling errors of theoretical methods. This can allow the system to operate at higher modulation formats and/or line-rates, increasing the overall network capacity potential. Finally, ML algorithms can be trained with real data in order to estimate the impact of various PLIs in cases when either there are no known analytical expressions or they are too complex, or even when component dependent sources of performance degradation are present, e.g., due to ageing of components, which cannot be easily incorporated into an analytical formalism. Further, in the works of [78, 79], centralized and distributed ML-based QoT estimation systems have been investigated with the aim of discovering QoT estimation models that are fine-tuned to the complex QoT specifications of each optical network slice. In particular, an SDN-based hybrid architecture comprised of a centralized controller as well as of multiple distributed controllers has been studied. A multiclass classifier is trained at the central controller of the centralized QoT system considering global network knowledge (i.e., according to all QoT requirements). Further, a collection of binary classifiers is trained in parallel and separately within a system of distributed controllers in the distributed QoT context. Each distributed controller stores information that is only applicable to its own slice sort, which is generated by links that meet the same QoT specifications. As a consequence, each distributed model learns to meet a different QoT requirement. According to results, the distributed approach outperforms the centralized one in modeling accuracy, accuracy per class and training time, particularly as the number of slice types increases. Further issues of interest and upcoming research concerning the ML-based QoT include:  
* The mathematical formulation of the proposed ML-based QoT estimation methods in order to tailor them to each specific problem. * The training of both the centralized and distributed QoT models on new datasets with more trends, while still considering various network loads. * The training of both the clustered and distributed QoT models using neural network (NN) topologies with varying numbers of hidden layers and units. Since the unified structure is built on a multiclass classification problem, which is generally more difficult to solve than its binary decompositions (distributed QoT problem), the analysis needs to be conducted on a broader NN topology. * The comparison of models and frameworks not only in terms of accuracy and CPU usage, but also in terms of utilization of other resources, e.g., memory utilization, routing and spectrum allocation (RSA). * The creation of dynamic multi-slice QoT-aware RSA heuristics that integrate various QoT models from both centralized and distributed systems.