A novel deep-learning-enabled QoS management scheme for encrypted traffic in software-defined cellular networks

Mahboob, T., Lim, J. W., Shah, S. T. and Chung, M. Y. (2022) A novel deep-learning-enabled QoS management scheme for encrypted traffic in software-defined cellular networks. IEEE Systems Journal, 16(2), pp. 2844-2855. (doi: 10.1109/JSYST.2021.3089175)

Abstract

Mobile users are served with over-the-top (OTT) services through their cellular networks. To ensure the privacy of users and confidentiality of content, most OTT service providers encrypt their traffic. When a cellular network has no information about the type of service, a default bearer may be created. However, the default bearer may not guarantee bandwidth to a service. Therefore, users may experience degraded service due to packet loss, delay, and reduced data rates. This article proposes a novel quality-of-service (QoS) management scheme for encrypted traffic in software-defined cellular networks. We introduce a deep-learning-enabled intelligent gateway to predict the service types of encrypted flows by considering statistical and QoS features. A QoS control manager maps the bearers to ongoing flows satisfying their QoS requirements. As a proof of concept, we implement a testbed considering encrypted traffic from the Tor network. Results indicate that the proposed scheme improves the network throughput by 41%, decreases packet loss, delay, and QoS violations by 51%, 21%, and 52%, respectively, and reduces the length and size of the queue at the base station compared to those of the conventional scheme. Moreover, the convolutional-neural-network-based classifier achieves higher accuracy, precision, recall, and F1 -score, as well as lower loss values, compared to the multilayer perceptron classifier.

• Item Type: Articles
• Additional Information: This work was supported by the Institute for Information and Communications Technology Promotion under Grant 2015-0-00567 (Development of Access Technology Agnostic Next-Generation Networking Technology for Wired-Wireless Converged Networks) funded by the Korea government (Ministry of Science, ICT and Future Planning).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Shah, Dr Syed Tariq
• Authors: Mahboob, T., Lim, J. W., Shah, S. T., and Chung, M. Y.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: IEEE Systems Journal
• Publisher: IEEE
• ISSN: 1932-8184
• ISSN (Online): 1937-9234
• Published Online: 05 July 2021