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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

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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
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