Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Unsupervised deep learning approaches <s> In this invited paper, my overview material on the same topic as presented in the plenary overview session of APSIPA-2011 and the tutorial material presented in the same conference [1] are expanded and updated to include more recent developments in deep learning. The previous and the updated materials cover both theory and applications, and analyze its future directions. The goal of this tutorial survey is to introduce the emerging area of deep learning or hierarchical learning to the APSIPA community. Deep learning refers to a class of machine learning techniques, developed largely since 2006, where many stages of non-linear information processing in hierarchical architectures are exploited for pattern classification and for feature learning. In the more recent literature, it is also connected to representation learning, which involves a hierarchy of features or concepts where higher-level concepts are defined from lower-level ones and where the same lower-level concepts help to define higher-level ones. In this tutorial survey, a brief history of deep learning research is discussed first. Then, a classificatory scheme is developed to analyze and summarize major work reported in the recent deep learning literature. Using this scheme, I provide a taxonomy-oriented survey on the existing deep architectures and algorithms in the literature, and categorize them into three classes: generative, discriminative, and hybrid. Three representative deep architectures – deep autoencoders, deep stacking networks with their generalization to the temporal domain (recurrent networks), and deep neural networks (pretrained with deep belief networks) – one in each of the three classes, are presented in more detail. Next, selected applications of deep learning are reviewed in broad areas of signal and information processing including audio/speech, image/vision, multimodality, language modeling, natural language processing, and information retrieval. Finally, future directions of deep learning are discussed and analyzed. <s> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Unsupervised deep learning approaches <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> BIB002 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Unsupervised deep learning approaches <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> BIB003
As we mentioned previously, deep architectures are classified into: 1) generative, 2) discriminative, and 3) hybrid models. We mentioned also that the generative models employ unsupervised learning approaches to characterize the high-order correlation properties of the input data BIB001 . The generative models need an unsupervised pre-training stage to extract the structures in the input data. They also need an additional top layer to perform the discriminative task BIB001 . In this paper, we discuss two deep generative models namely stacked auto-encoder (SAE) and deep belief network (DBN), due to the fact that they were already employed in SDNs BIB002   BIB003 .