A Novel SDN-Based Application-Awareness Mechanism by Using Deep Learning

Abstract

With the rapid development of the Internet of Things (IoT) and smart cities, more and more types of applications have been emerging. In fact, different applications have different features and different requirements on services. In order to satisfy users' Quality of Service (QoS) requirements, the application-awareness technique should be leveraged to distinguish different applications for providing the differentiated services. However, the traditional Internet only can obtain the local network view, which belongs to the offline awareness mode and cannot adapt to the dynamical network environment. At the right time, Software-Defined Networking (SDN) has been accepted as a new networking paradigm thanks to its network awareness on the global status information, which can greatly facilitate the online applicationawareness. At present, three ways, i.e., port number, depth packet inspection and deep learning can be used for the application-awareness. To the best of our knowledge, the deep learning based application-awareness method is the most cutting-edge technique. In spite of this, the previous related schemes fail to effectively guarantee the correctness and stability. To this end, this paper proposes a Convolutional Neural Network (CNN) based deep learning mechanism to do the application-awareness, including three phases, i.e., traffic collection, data pre-processing and application-awareness. The SDN environment is implemented based on the MiniNet and the simulation experiments are made based on the TensorFlow. The experimental results show that the proposed application-awareness mechanism outperforms three benchmarks on recall ratio, precision ratio, F value and stability.

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“…This section makes simulations based on SDICN network structure and CNN training model. At first, the experimental results on CNN training are shown in Table 1, including recall ratio, precision ratio and F1 value 9 …”

Information‐centric networking driven psychology recognition and health conduction of undergraduates

“…The recall ratio, precision ration and F1 value are shown in Table 1, where the definitions of three metrics can be found from Reference 10. It is observed that, the adopted DBN neural network structure in this letter has the highest recall ratio, precision ratio and F1 value, which indicates that DBN has the best psychology recognition effect in terms of the collected psychology data of 4000 undergraduates.…”

Intelligent Traffic Management in Next-Generation Networks

“…The comparative analysis against the individual single classifier and voting ensemble demonstrates the effectiveness of the proposed ensemble. As DL helps to eliminate the manual feature engineering task, Hu et al [83] proposed a CNN-based deep learning method to address the SDN-based application awareness called CDSA. As shown in Figure 6, CDSA consists of three components, which are traffic collection, data pre-processing, and application awareness.…”