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 application-awareness. 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.
Highlights
In the traditional Internet, the network only supports the best effort services and fails to provide the differentiated services, that is, all applications have a fair competition on the limited network resources [1]
The deep learning based application-awareness technique usually depends on the feature statistics of traffic which can be obtained by the header of packet rather than the whole packet, its consumed time is smaller than that consumed by Depth Packet Inspection (DPI) [13]
This paper proposes a Convolutional Neural Network (CNN) [17], [18] based deep learning mechanism which belongs to the unsupervised learning strategy to do the application-awareness
Summary
In the traditional Internet, the network only supports the best effort services and fails to provide the differentiated services, that is, all applications have a fair competition on the limited network resources [1]. The deep learning based application-awareness technique usually depends on the feature statistics of traffic which can be obtained by the header of packet rather than the whole packet, its consumed time is smaller than that consumed by DPI [13]. The previous deep learning based application-awareness schemes fail to effectively guarantee the correctness and stability. To this end, this paper proposes a Convolutional Neural Network (CNN) [17], [18] based deep learning mechanism which belongs to the unsupervised learning strategy to do the application-awareness. This paper uses the CNN-based Deep learning method to address the SDN-based Application-awareness (CDSA), and the major contributions are summarized as follows.
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