Performance analysis of the ANGEL system for automated control of game traffic prioritisation

The Automated Network Games Enhancement Layer (ANGEL) is a novel architecture for meeting Quality of Service (QoS) requirements of real-time network game traffic across consumer broadband links. ANGEL utilises detection of game traffic in the ISP network via the use of Machine Learning techniques and then uses this information to inform network routers---in particular the home access modem where bandwidth is limited---of these flows such that the traffic may be prioritised. In this paper we present the performance characteristics of the fully built ANGEL system. In particular we show that ANGEL is able to detect game traffic with better than 96% accuracy and effect prioritisation within a second of game flow detection. We also demonstrate the processing performance of key ANGEL components under typical hardware scenarios.