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

Practical machine learning based multimedia traffic classification for distributed QoS management

Zander, S. and Armitage, G. (2011) Practical machine learning based multimedia traffic classification for distributed QoS management. In: 2011 IEEE 36th Conference on Local Computer Networks (LCN), 4 - 7 October 2011, Hilton Hotel Bonn, Germany

PDF - Published Version
Download (235kB) | Preview
Link to Published Version:
*Subscription may be required


A multi-service Internet requires routers to recognise and prioritise IP flows carrying interactive or multimedia traffic. It is increasingly problematic for legal or administrative reasons to recognise such flows using unique port numbers or deep packet inspection. New work in recent years shows that Machine Learning (ML) techniques can use externally observable statistical characteristics to usefully differentiate such IP traffic. However, most previous work has not addressed the practicality of ML-based traffic classification in terms of CPU and memory usage. Here we describe our design, implementation and performance evaluation of a distributed, ML-based traffic classification and control system for FreeBSD's IP Firewall (IPFW). On an Intel Core i7 2.8 GHz PC our system can classify up to 400 000 packets per second using only one core and our system scales well to up to 100 000 simultaneous flows. Also our implementation allows one classifier PC to control subsequent traffic shaping or blocking at multiple (potentially lower performance) routers or gateways distributed around the network.

Item Type: Conference Paper
Item Control Page Item Control Page


Downloads per month over past year