# Using Convolutional Neural Networks for Blocking Prediction in Elastic Optical Networks
###### Abstract  
This paper presents a study on connection-blocking prediction in Elastic Optical Networks (EONs) using Convolutional Neural Networks (CNNs). In EONs, connections are established and torn down dynamically to fulfill the instantaneous requirements of the users. The dynamic allocation of the connections may cause spectrum fragmentation and lead to network performance degradation as connection blocking increases. Predicting potential blocking situations can be helpful during EON operations. For example, this prediction could be used in real networks to trigger proper spectrum defragmentation mechanisms at suitable moments, thereby enhancing network performance. Extensive simulations over the well-known NSFNET (National Science Foundation Network) backbone network topology were run by generating realistic traffic patterns. The obtained results are later used to train the developed machine learning models, which allow the prediction of connection-blocking events. Resource use was continuously monitored and recorded during the process. Two different Convolutional Neural Network models, a 1D CNN (One-Dimensional Convolutional Neural Network) and 2D CNN (Two-Dimensional Convolutional Neural Network), are proposed as the predicting methods, and their behavior is compared to other conventional models based on an SVM (Support Vector Machine) and KNN (K Nearest Neighbors). The results obtained show that the proposed 2D CNN predicts blocking with the best accuracy (92.17%), followed by the SVM, the proposed 1D CNN, and KNN. Results suggest that 2D CNN can be helpful in blocking prediction and might contribute to increasing the efficiency of future EON networks. s +
Footnote †: journal: Medical  
frequency and fixed wavelength spacing of typically 50 GHz or 100 GHz. However, in Fixed Grid mode, bandwidths cannot be adjusted flexibly to the requirements of heterogeneous connections [5]. Therefore, a main drawback of WDM networks is the waste of spectrum when the traffic assigned to optical fiber channels is below its potential capacity. These shortcomings of Fixed Grid wavelength networks necessitate a more flexible spectrum assignment scheme. Elastic Optical Networks (EONs) have been proposed as a means to tackle the future Internet's enormous capacity and heterogeneous traffic requirements [6; 7]. EONs improve flexible spectrum allocation by assigning the bandwidth more efficiently than is the case in WDM networks. EONs allow the bandwidth assignment to be flexibly adapted to the heterogeneous sizes of connections while using advanced modulation formats to even improve the efficient use of the spectrum [8; 9]. These networks support fractional data rates and flexible, granular traffic via the sub-wavelength and super-wavelength channels [10]. EONs improve the efficiency of spectrum usage [3; 10], so that, in principle, more spectrum is available for allocating new connection requests. A lightpath is an optical connection between two nodes in an optical network. Occasionally, there is no direct fiber connection between both nodes, requiring more than one link to connect them. The same frequency slots should be employed on all hops in the end-to-end path of the connection. This characteristic is known as the wavelength continuity constraint. Also, in optical networks, a set of contiguous spectrum slots should be allocated to each connection, which is known as a contiguous constraint [11]. To meet the heterogeneous requirements, the spectral granularity in the fibers of the path between both end nodes must be the same. In EONs, the size of the spectral frequency slots (FSs) is typically 12.5 or 6.25 GHz. When compared with traditional WDM networks (Figure 1), this finer granularity allows a better fitting of connection demands in the available spectrum. The network performance is therefore optimized through the dynamic reallocation of the frequency slots. Typically, in EONs, the size of the connections does not change after the connection has been established.