Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Unsupervised deep learning approaches in SDN <s> Recent years, Software Defined Routers (SDRs) (programmable routers) have emerged as a viable solution to provide a cost-effective packet processing platform with easy extensibility and programmability. Multi-core platforms significantly promote SDRs’ parallel computing capacities, enabling them to adopt artificial intelligent techniques, i.e., deep learning, to manage routing paths. In this paper, we explore new opportunities in packet processing with deep learning to inexpensively shift the computing needs from rule-based route computation to deep learning based route estimation for high-throughput packet processing. Even though deep learning techniques have been extensively exploited in various computing areas, researchers have, to date, not been able to effectively utilize deep learning based route computation for high-speed core networks. We envision a supervised deep learning system to construct the routing tables and show how the proposed method can be integrated with programmable routers using both Central Processing Units (CPUs) and Graphics Processing Units (GPUs). We demonstrate how our uniquely characterized input and output traffic patterns can enhance the route computation of the deep learning based SDRs through both analysis and extensive computer simulations. In particular, the simulation results demonstrate that our proposal outperforms the benchmark method in terms of delay, throughput, and signaling overhead. <s> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Unsupervised deep learning approaches in SDN <s> In information-centric networking, accurately predicting content popularity can improve the performance of caching. Therefore, based on software defined network (SDN), this paper proposes Deep-Learning-based Content Popularity Prediction (DLCPP) to achieve the popularity prediction. DLCPP adopts the switch’s computing resources and links in the SDN to build a distributed and reconfigurable deep learning network. For DLCPP, we initially determine the metrics that can reflect changes in content popularity. Second, each network node collects the spatial-temporal joint distribution data of these metrics. Then, the data are used as input to stacked auto-encoders (SAE) in DLCPP to extract the spatiotemporal features of popularity. Finally, we transform the popularity prediction into a multi-classification problem through discretizing the content popularity into multiple classifications. The Softmax classifier is used to achieve the content popularity prediction. Some challenges for DLCPP are also addressed, such as determining the structure of SAE, realizing the neuron function on an SDN switch, and deploying DLCPP on an OpenFlow-based SDN. At the same time, we propose a lightweight caching scheme that integrates cache placement and cache replacement—caching based on popularity prediction and cache capacity (CPC). Abundant experiments demonstrate good performance of DLCPP, and it achieves close to 2.1%~15% and 5.2%~40% accuracy improvements over neural networks and auto regressive, respectively. Benefitting from DLCPP’s better prediction accuracy, CPC can yield a steady improvement of caching performance over other dominant cache management frameworks. <s> BIB002
Mao et al. BIB001 proposed an approach for construction of routing table based on deep belief networks. The experimental results showed that the proposed approach outperforms traditional open shortest path first (OSPF) protocol in terms of throughout and average delay per hope. In addition, the experimental results showed that the proposed routing method can run more than 100 times faster on a GPU than on a CPU. Zhang et al.  introduced a hybrid deep neural network for SDN-based network application classification, which consists of the stacked auto-encoder and softmax regression layer. SAE was used as unsupervised-learning-based feature extractor, whereas softmax regression was used as a supervised classifier. The proposed model achieved higher classification accuracy over SVM in terms of accuracy, precision, recall and F1-measure. Niyaz et al.  proposed a DDoS detection system based on SAE as feature extractor in SDN. A softmax layer is also used for classification. Their proposed system can identify DDoS attacks with an accuracy of 95.65% for 8-class classification. It also achieved an accuracy of 99.82% for binary classification to show whether an attack happened or not with very low false-positive (0.3%). It also outperformed soft-max and neural network classifiers. Liu et al. BIB002 SAE was used for extracting spatio-temporal features of content popularity. Then they also used softmax classifier for the classification task. Their model achieved 2.1%-15% and 5.2%-40% accuracy improvements over neural networks and auto regressive, respectively.