TrafficVision: A Case for Pushing Software Defined Networks to Wireless Edges
Mostafa Uddin, Tamer Nadeem · 2016
In wireless network edges, knowing the network flow types and applications can enable various policy-driven network managements (i.e. traffic offloading, BYOD, E2E QoS etc.). However, applying network policies at wireless links between the mobile devices and access points (APs) requires greater visibility and control on generated traffic generated from mobile devices. The recent advent of Software-Defined Networking (SDN) could enable fine-grained network management at the edge. However, in existing solutions, SDN uses external Deep-Packet Inspection (DPI) engine that requires additional and potentially heavy loaded computational resources to perform the packet analysis. Moreover, mobile applications become more dynamic (rapid install/update), diverse and complex (individual applications generate multiple traffic types) in which scalability and granularity requirements challenging current DPI solutions. In addition, DPI is unreliable in classifying application's encrypted packets. Therefore, in this paper we present the design and the development of TrafficVision that extends the SDN's layer architecture to have fine-grained and real-time policy making at wireless network edges. More specifically, we carefully extends the SDN framework to develop tools that allow to have scalable, efficient and flexible way to classify the network traffic flows at fine-grained fashion using Machine-Learning (ML) based technique. We evaluate our system using the performance of CPU utilization, network overhead and network throughput metrics. Finally, as a proof of concept, we develop a simple case study of traffic management application that exploits TrafficVision.