Network traffic classification with machine learning and edge computing

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With the emerging prevalence of data-hungry applications, such as augmented reality, wireless multimedia, cloud computing, and Internet of Things, the current generation of network infrastructure will face severe challenges in its struggle to satisfy the exploding service demands. In order to efficiently allocate the available networking resources among these diverse set of applications, traffic classification has become an essential technology for the optimal operation of a network. However, classical classification methods, such as those based on network protocol interface or packet content inspection, are no longer suitable to meet the accuracy, delay, and privacy requirements to support modern applications and services. The ever changing characteristics of new applications and increasing volume of traffic demand a flexible and automatic approach. In this project, we will apply dynamic machine learning techniques to identify and categorize the network traffic of an Internet service provider. A unique feature of this project is that we will leverage the availability of a vast amount anonymous user traffic data from TELUS, to investigate into a hybrid combination of both supervised and unsupervised learning. We will also take advantage of the emerging capabilities of computing at the network edge, where the network traffic is more localized with shared commonalities among local users, to improve the accuracy of traffic classification. Through mathematical analysis, computer simulation, and large-scale data experimentation, we will generate practical guidelines on how to design and operate network traffic classifiers, to optimally balance the tradeoff between accuracy, privacy, cost, and delay. The outcomes of the proposed research are expected to benefit both our industry partner and the Canadian information and communication industry at large, by promoting engineering theory, technical methods, and standardization policies that can lead to system improvement, cost reduction, sustainable growth, and long-term competitiveness.