# Artificial Intelligence Enabled Routing in Software Defined Networking
## 1 Introduction
### Artificial Intelligence Enabled Routing  
Notably, in the recent decade, performance has improved in numerous fields with the application of artificial intelligence (AI) technology [13]. The networking field is no exception. The well-known Turing test [14] considers machines intelligent if humans cannot distinguish them from a man when talking to them. In 1980, [PERSON] classified AI into strong and weak types [15]. A strong AI has a complex algorithm that can help it act in different situations, whereas the actions of a weak AI are preprogrammed by a person. In other words, strong AI-enabled machines have the ability to apply intelligence to any problem rather than only specific problems, whereas weak AI-enabled machines can only simulate human behavior. As illustrated in Figure 3, machine learning (ML), which describes a methodology implementing AI, is a growing subfield of AI [16]. Support vector machines (SVMs) [17], decision trees [18], random forests [19], artificial neural networks (ANNs) [20], and so on have been presented sequentially as ML technologies. As to deep learning (DL), it is a family of machine learning methods based on ANNs with representation learning. Learning can be supervised, semi-supervised, or unsupervised. The \"deep\" in DL comes from that two or more hidden layers are used in the network. In other words, an unbounded number of layers of bounded size constitute an ANN model, which allows practical application and optimized implementation while retaining theoretical universality under mild conditions [21]. Figure 1: Software defined networking (SDN) architecture. Figure 2: Main components of flow entry. An ANN mainly simulates human brain nerves using mathematical models. In contrast to human brain nerves (see Figure 4a), inputs \(x_{1}\), \(x_{2}\), and \(x_{3}\) of an ANN (see Figure 4b) simulate human brain nerve dendrites; weights \(w_{1}\), \(w_{2}\), and \(w_{3}\) of an ANN simulate axons; and the deviation value of an ANN (denoted by \(b\)) simulates a synapse, which is a critical action value of a neuron. Equation (1) represents the operation mode of one neuron, where \(y\) denotes the output. To handle complex nonlinear problems, Equation (1) is further substituted with an activation function to form a differentiable nonlinear function, as expressed by Equation (2). Through the backpropagation algorithm (BPA) [22], the optimization gradient descent method can be used to continuously iteratively compute and update the weights in the ANN model such that the model output can approach the expected value, that is, the corresponding label data (designated as \(y_{i}\)), until the error function (see Equation (3)) converges. \[y=x_{1}w_{1}+x_{2}w_{2}+x_{3}w_{3} \tag{1}\]  
\[y=Activation\ function(x_{1}w_{1}+x_{2}w_{2}+x_{3}w_{3}) \tag{2}\]  
\[Loss\ function=\sum_{i=0}{(y_{i}-y_{i}^{\prime})^{2}} \tag{3}\]  
where \(i\) indicates the \(i\)-th record of training data. Since the layers of an ANN in DL are allowed to be heterogeneous and to deviate widely from biologically informed connectionist models for the sake of efficiency, trainability, and understandability, this paper presents an AI-enabled routing (AIER) mechanism with congestion avoidance by introducing  
Figure 4: (**a**) A neuron of Human brain nerves; (**b**) ANN model for simulating a neuron. Figure 3: Evolution of artificial intelligence (AI). an ANN model in the control plane of SDN for intelligent path selection. The successful application of ANNs for the AIER not only can alleviate the impact of monitoring periods with dynamic routing but also can provide the learning ability to make superior routing decisions from past experiences. It is also demonstrated that the proposed AIER outperforms the SDN routing schemes with no AI application via simulations. The significant improvement in the average throughput, packet loss ratio, and packet delay versus data rate for different monitoring periods in the system can be observed. The remainder of this paper is organized as follows.