Murdoch University Research Repository

Welcome to the Murdoch University Research Repository

The Murdoch University Research Repository is an open access digital collection of research
created by Murdoch University staff, researchers and postgraduate students.

Learn more

Sub-flow packet sampling for scalable ML classification of interactive traffic

Zander, S., Nguyen, T. and Armitage, G. (2012) Sub-flow packet sampling for scalable ML classification of interactive traffic. In: 2012 IEEE 37th Conference on Local Computer Networks (LCN), 22 - 25 Oct 2012, Clearwater, Florida

Link to Published Version:
*Subscription may be required


Machine Learning (ML) classifiers have been shown to provide accurate, timely and continuous IP flow classification when evaluating sub-flows (short moving windows of packets within flows). They can be used to provide automated QoS management for interactive traffic, such as fast-paced multiplayer games or VoIP. As with other ML classification approaches, previous sub-flow techniques have assumed all packets in all flows are being observed and evaluated. This limits scalability and poses a problem for practical deployment in network core or edge routers. In this paper we propose and evaluate subflow packet sampling (SPS) to reduce an ML sub-flow classifier's resource requirements with minimal compromise of accuracy. While random packet sampling increases classification time from <;1 second to over 30 seconds and can reduce accuracy from 98% to <;90%, our tailored SPS technique retains classification times of <;1 second while providing 98% accuracy.

Item Type: Conference Paper
Item Control Page Item Control Page