Video Streaming Service Identification on Software-Defined Networking

Abstract

It is well known that video streaming is the major network traffic today. Furthermore, the traffic generated by video streaming is expected to increase exponentially. On the other hand, Software-Defined Networking (SDN) has been considered a viable solution to cope with the complexity and increasing network traffic due to its centralised control and programmability features. These features, however, do not guarantee that network performance will not suffer as traffic grows. As result, understanding video traffic and optimising video traffic can aid in control various aspects of network performance, such as bandwidth utilisation, dynamic routing, and Quality of Service (QoS). This paper presents an approach to identify video streaming traffic in SDN and investigates the feasibility of using Knowledge-Defined Networking (KDN) in traffic classification. KDN is a networking paradigm in SDN, that utilises Artificial Intelligence (AI) using Machine Learning methods, allowing for integrating behavioural models to recognise patterns in SDN traffic, such as video streaming traffic detection. We extract important network traffic information in the form of flows statistics in our initial proof-of-concept. Then, we used such information to train six ML models that can classify network traffic into three types, Video on Demand (VoD), Livestream, and no-video traffic. Our proof-of-concept demonstrates that our approach is applicable and that we can identify and classify video streaming traffic with 97.5% accuracy using the Decision Tree model.