Identifying Traffic Differentiation in Mobile Networks

Arash Molavi Kakhki, Abbas Razaghpanah, Anke Li, Hyungjoon Koo, Rajesh Golani, David Choffnes, Phillipa Gill, Alan Mislove
2015 Proceedings of the 2015 ACM Conference on Internet Measurement Conference - IMC '15  
Traffic differentiation-giving better (or worse) performance to certain classes of Internet traffic-is a well-known but poorly understood traffic management policy. There is active discussion on whether and how ISPs should be allowed to differentiate Internet traffic [8, 21] , but little data about current practices to inform this discussion. Previous work attempted to address this problem for fixed line networks; however, there is currently no solution that works in the more challenging mobile
more » ... environment. In this paper, we present the design, implementation, and evaluation of the first system and mobile app for identifying traffic differentiation for arbitrary applications in the mobile environment (i.e., wireless networks such as cellular and WiFi, used by smartphones and tablets). The key idea is to use a VPN proxy to record and replay the network traffic generated by arbitrary applications, and compare it with the network behavior when replaying this traffic outside of an encrypted tunnel. We perform the first known testbed experiments with actual commercial shaping devices to validate our system design and demonstrate how it outperforms previous work for detecting differentiation. We released our app and collected differentiation results from 12 ISPs in 5 countries. We find that differentiation tends to affect TCP traffic (reducing rates by up to 60%) and that interference from middleboxes (including video-transcoding devices) is pervasive. By exposing such behavior, we hope to improve transparency for users and help inform future policies.
doi:10.1145/2815675.2815691 dblp:conf/imc/KakhkiRLKGCGM15 fatcat:l22fqogje5gfzoexzn3ds4cxme