MoViDiff: Enabling service differentiation for mobile video apps

Abstract

Among the mobile applications contributing to the surging Internet traffic, video applications are some of the biggest contributors. Most of these video applications use HTTP/HTTPS tunneling making it difficult to apply port based or packet data based identification of flows. This makes it challenging for network operators to enforce bandwidth regulation policies for app based service differentiation due to lack of flow identification mechanisms for mobile apps. We explore a packet data agnostic feature of video flows, namely packet-size, to identify the flows. We show that it is possible to train a classifier that can distinguish packets from streaming and interactive video apps with high accuracy. We design and implement a system, called MoViDiff, with this classifier at the core, that allows bandwidth regulation between video traffic of two different categories, streaming and interactive. We show that we can achieve an average accuracy of 96% in classifying the traffic, with the maximum accuracy reaching as high as 98%.