Application-awareness in SDN

  • Authors:

  • Zafar Ayyub Qazi;Jeongkeun Lee;Tao Jin;Gowtham Bellala;Manfred Arndt;Guevara Noubir

  • Affiliations:

  • Stony Brook University, Stony Brook, NY, USA;HP Labs, Palo Alto, CA, USA;Qualcomm Research, San Diego, CA, USA;HP Labs, Palo Alto, CA, USA;HP Networking, Sacramento, CA, USA;Northeastern University, Boston, MA, USA

  • Venue:
  • Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.