Machine Learning
Networked games: a QoS-sensitive application for QoS-insensitive users?
RIPQoS '03 Proceedings of the ACM SIGCOMM workshop on Revisiting IP QoS: What have we learned, why do we care?
The effects of loss and latency on user performance in unreal tournament 2003®
Proceedings of 3rd ACM SIGCOMM workshop on Network and system support for games
Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Networking and Online Games
Analysis of factors affecting players' performance and perception in multiplayer games
NetGames '05 Proceedings of 4th ACM SIGCOMM workshop on Network and system support for games
Automated Traffic Classification and Application Identification using Machine Learning
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
ACM SIGCOMM Computer Communication Review
Automated network games enhancement layer: a proposed architecture
NetGames '06 Proceedings of 5th ACM SIGCOMM workshop on Network and system support for games
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A Modular Machine Learning System for Flow-Level Traffic Classification in Large Networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Timely and continuous machine-learning-based classification for interactive IP traffic
IEEE/ACM Transactions on Networking (TON)
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The Automated Network Games Enhancement Layer (ANGEL) [6] 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.