Nigel Williams

A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification

The identification of network applications through observation of associated packet traffic flows is vital to the areas of network management and surveillance. Currently popular methods such as port number and payload-based identification exhibit a number of shortfalls. An alternative is to use machine learning (ML) techniques and identify network applications based on per-flow statistics, derived from payload-independent features such as packet length and inter-arrival time distributions. The performance impact of feature set reduction, using Consistency-based and Correlation-based feature selection, is demonstrated on Naïve Bayes, C4.5, Bayesian Network and Naïve Bayes Tree algorithms. We then show that it is useful to differentiate algorithms based on computational performance rather than classification accuracy alone, as although classification accuracy between the algorithms is similar, computational performance can differ significantly.

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

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.

Automated network games enhancement layer: a proposed architecture

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.