# Using Convolutional Neural Networks for Blocking Prediction in Elastic Optical Networks
###### Abstract
As an example, if three FSs are requested for a connection between two nodes, then the network should allocate three FSs to the connection until that connection is finalized [12; 13]. In EONs, the size of a connection can vary from one FS (assuming a size of 12.5 GHz, it would be enough to allocate a 10 Gbit/s connection) to a large number of FSs when very high bit-rate (e.g., 400 Gbit/s) connections have to be established [14; 15]. New connections might be dynamically added to or removed from the network fibers available in the spectrum while the network is in operation. When a new connection is requested, and once the end-to-end path has been found using any routing algorithm, a specific number of contiguous free FSs are assigned to that connection, depending on the required bandwidth. This process is called spectrum assignment (SA). Typically, a \"First Fit\" approach is used in the SA process [15], which means assigning the first available slots as far to the left as possible. After the connection has ended, the allocated spectrum is released (this process is illustrated in Figure 2). Figure 1: Spectrum allocation in a conventional WDM network. The ability of EONs to accommodate multiple connections of different sizes results in a major improvement in the spectrum efficiency of the network. However, the dynamic nature of the establishing and ending of the connections of random sizes can lead to a disordered spectrum. Depending on the traffic profiles, voids of different sizes can be found at any instant during network operation. This effect is known as spectrum fragmentation [15], which can prevent the spectrum from being utilized in an optimum way. Figure 2 also illustrates an example of spectrum fragmentation. In this image, the allocated spectrum in an optical link at three different time instants is shown. After time instant t1, connection C2 (with two FS sizes) and connection C4 (with five FSs) are complete. However, even though seven FSs are released, and there are 11 free slots, it is not possible to allocate a seven-FS connection or even a six-FS connection in this link. The reason is that, for allocating a six-FS connection, assuming one FS is left as the guard band, a contiguous free space with a length of eight FSs is required. If C3 could be re-allocated to the left at t = t2, it would be possible to allocate a connection of up to eight slots. Spectrum fragmentation is a major problem in Elastic Optical Networks (EONs). If the spectrum fragmentation is not appropriately managed, it can result in network blocking. Therefore, the network may not be able to accommodate future connection requests even though free slots are available. The use of machine learning (ML) approaches is proposed as a potential solution to mitigate this problem. In recent years, the application of machine learning for spectrum allocation has been investigated in several research publications. The authors in [1] attempted to predict the peak traffic in networks that can affect the routing decisions using a Graph Convolutional Network and Generative Adversarial Network (GCN-GAN). The GCN-GAN model addresses optical network traffic problems by predicting the peak traffic in the network that potentially can be used in dynamically optimizing the spectrum allocation algorithm. To forecast the temporal link dynamics in weighted dynamic networks, the authors of [12] presented and assessed a non-linear-termed GCN-GAN model. In order to record changing patterns in successive graph time slices and train a generative adversarial model to generate projections of future states, their approach mixes GCNs with Long Short-Term Memory (LSTM) networks. Although they have yet to put their findings into practice, the authors claim that the GCN-GAN beats six contenders, including a generic LSTM. A Back Propagation Neural Network (BPNN) is another ML-based approach for spectrum provisioning [13]. This algorithm is relatively simple to implement in computers.