Sunday, August 18, 2013
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.