Research: Crowd Source as a Base for SDN Application Awareness

A paper by Zafar Qazi, Stony Brook University; Jeongkeun Lee, HP Labs; Tao Jin, Qualcomm Research; Gowtham Bellala, HP Labs; Manfred Arndt, HP Networking; Guevara Noubir [pictured],  Northeastern University present application awareness into Software-Defined Networking (SDN),

See "Application-Awareness in SDN" - here.

ABSTRACT (see poster below)

We present a framework, Atlas, which incorporates application awareness into Software-Defined Networking (SDN), which is currently capable of L2/3/4-based policy enforcement but agnostic to higher layers. Atlas enables fine-grained, accurate and scalable application classification in SDN. It employs a machine learning (ML) based traffic classification technique, a crowd-sourcing approach to obtain ground truth data and leverages SDN’s data reporting mechanism and centralized control. We prototype Atlas on HP Labs wireless networks and observe 94% accuracy on average, for top 40 Android applications.