Machine Learning
Measurement and classification of out-of-sequence packets in a tier-1 IP backbone
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
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
Dissecting server-discovery traffic patterns generated by multiplayer first person shooter games
NetGames '05 Proceedings of 4th ACM SIGCOMM workshop on Network and system support for 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
Performance analysis of the ANGEL system for automated control of game traffic prioritisation
Proceedings of the 6th ACM SIGCOMM workshop on Network and system support for games
Timely and continuous machine-learning-based classification for interactive IP traffic
IEEE/ACM Transactions on Networking (TON)
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In this paper we present the design of the Automated Network Games Enhancement Layer (ANGEL), a novel architecture for meeting Quality of Service (QoS) requirements of real-time network game traffic across consumer broadband links. Consumer access links can become bottlenecks when faced with heterogeneous network traffic (e.g. simultaneous use of online games and peer-to-peer file sharing) and the online gaming experience can be significantly affected by bottleneck queuing. Implementing QoS on these links provides improvement by reducing latency and jitter. In our approach network servers automatically identify traffic that might benefit from QoS and then trigger provisioning of QoS by signaling network elements such as access routers. By placing intelligence within the network, QoS decisions can be transparently made for the game applications without imposing an additional processing cost at the access link router. Our system uniquely uses machine learning methods to perform traffic classification.