Network traffic classification with machine learning and edge computing

随着增强现实、无线多媒体、云计算和物联网等数据需求型应用的兴起，当前一代网络基础设施在满足爆炸式增长的服务需求方面将面临严峻的挑战。为了在这些不同的应用程序之间有效地分配可用的网络资源，流量分类已成为网络优化运行的一项重要技术。然而，经典的分类方法，如基于网络协议接口或分组内容检测的方法，不再适合于满足支持现代应用和服务的准确性、延迟和隐私要求。新应用程序不断变化的特性和不断增加的流量需要灵活和自动的方法。在这个项目中，我们将应用动态机器学习技术来识别和分类互联网服务提供商的网络流量。该项目的一个独特之处在于，我们将利用TELUS提供的大量匿名用户流量数据，研究监督学习和无监督学习的混合组合。我们还将利用网络边缘的新兴计算能力，其中网络流量更加本地化，本地用户之间共享共性，以提高流量分类的准确性。通过数学分析，计算机模拟和大规模数据实验，我们将生成关于如何设计和操作网络流量分类器的实用指南，以最佳地平衡准确性，隐私性，成本和延迟之间的权衡。拟议研究的结果预计将有利于我们的行业合作伙伴和加拿大信息和通信行业，通过促进工程理论，技术方法和标准化政策，可以导致系统改进，降低成本，可持续增长和长期竞争力。

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.