Online multimedia traffic classification from the QoS perspective using deep learning

Abstract

In the next generation of communication systems, data traffic is expected to increase dramatically and continuously. Particularly for multimedia traffic, it has a dominant share in the increasing traffic. Therefore, there is an urgent need to develop an effective and accurate scheme to achieve online and automatic traffic management. To this end, this paper proposes an online multimedia traffic classification framework based on a Convolutional Neural Network (CNN), capable of conducting fast and early classification as well as class incremental learning. First, the sliding window technique is applied to capture the flow slices for further feature extraction. Then, the 3-dimensional flow representation is extracted based on the probability distribution function. After that, according to the specific structure of features, a deeply adapted structure of CNN is devised to better learn the knowledge from the representation. Besides, to better support the addition of new services, a class incremental learning model is developed with the techniques of knowledge distillation and bias correction to achieve continuous learning without retraining from scratch. Our experimental results reveal that the proposed method achieves faster and more accurate traffic classification compared with the state-of-the-art. Additionally, the deployed scheme using incremental learning achieves drops by about 50% in both time and memory consumptions compared with existing methods, while guaranteeing the accurate classification after adding new classes.