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.
['Zafar Ayyub Qazi', 'Jeongkeun Lee', 'Tao Jin', 'Gowtham Bellala', 'Manfred R. Arndt', 'Guevara Noubir']
Application-awareness in SDN