Arash Molavi Kakhki

Identifying Traffic Differentiation in Mobile Networks

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, 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 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.

BingeOn Under the Microscope: Understanding T-Mobiles Zero-Rating Implementation

The popularity of mobile devices for ubiquitous Internet access has led to exploding demand for relatively scarce cellular bandwidth. As a result, cellular operators increasingly turn to creative ways to manage their customers’ demand on capacity, using traffic shaping and transcoding and zero-rating. With zero-rating, Internet Service Providers (ISPs) do not charge users for traffic sent to/from certain services, often because those services agree to use limited bandwidth resources. Perhaps the most well-publicized of case in the U.S. is T-Mobile’s “BingeOn ” service, which zero-rates video streams from a large number of partner sites. In this paper, we address this issue by conducting a case study of T-Mobile’s zero-rating policy to understand its implications for users and content providers in terms of data quota, performance, and QoE. We focus on T-Mobile and BingeOn due to their recent controversy, but we believe that lessons learned from this exercise will readily apply to other carriers using similar technologies to implement their policies.

Taking a long look at QUIC: an approach for rigorous evaluation of rapidly evolving transport protocols

Googles QUIC protocol, which implements TCP-like properties at the application layer atop a UDP transport, is now used by the vast majority of Chrome clients accessing Google properties but has no formal state machine specification, limited analysis, and ad-hoc evaluations based on snapshots of the protocol implementation in a small number of environments. Further frustrating attempts to evaluate QUIC is the fact that the protocol is under rapid development, with extensive rewriting of the protocol occurring over the scale of months, making individual studies of the protocol obsolete before publication. Given this unique scenario, there is a need for alternative techniques for understanding and evaluating QUIC when compared with previous transport-layer protocols. First, we develop an approach that allows us to conduct analysis across multiple versions of QUIC to understand how code changes impact protocol effectiveness. Next, we instrument the source code to infer QUICs state machine from execution traces. With this model, we run QUIC in a large number of environments that include desktop and mobile, wired and wireless environments and use the state machine to understand differences in transport- and application-layer performance across multiple versions of QUIC and in different environments. QUIC generally outperforms TCP, but we also identified performance issues related to window sizes, re-ordered packets, and multiplexing large number of small objects; further, we identify that QUICs performance diminishes on mobile devices and over cellular networks.

lib•erate, (n): a library for exposing (traffic-classification) rules and avoiding them efficiently

Middleboxes implement a variety of network management policies (e.g., prioritizing or blocking traffic) in their networks. While such policies can be beneficial (e.g., blocking malware) they also raise issues of network neutrality and freedom of speech when used for application-specific differentiation and censorship. There is a poor understanding of how such policies are implemented in practice, and how they can be evaded efficiently. As a result, most circumvention solutions are brittle, point solutions based on manual analysis. This paper presents the design and implementation of lib•erate, a tool for automatically identifying middlebox policies, reverse-engineering their implementations, and adaptively deploying custom circumvention techniques. Unlike previous work, our approach is application-agnostic, can be deployed unilaterally (i.e., only at one endpoint) on unmodified applications via a linked library or transparent proxy, and can adapt to changes to classifiers at runtime. We implemented a lib•erate prototype as a transparent proxy and evaluate it both in a testbed environment and in operational networks that throttle or block traffic based on DPI-based classifier rules, and show that our approach is effective across a wide range of middlebox deployments.

Identifying traffic differentiation on cellular data networks

The goal of this research is to detect traffic differentiation in cellular data networks. We define service differentiation as any attempt to change the performance of network traffic traversing an ISPs boundaries. ISPs may implement differentiation policies for a number of reasons, including load balancing, bandwidth management, or business reasons. Specifically, we focus on detecting whether certain types of network traffic receive better (or worse) performance. As an example, a wireless provider might limit the performance of third-party VoIP or video calling services (or any other competing services) by introducing delays or reducing transfer rates to encourage users to use services provided by the wireless provider. Previous work explored this problem in limited environments. Glasnost focused on BitTorrent in the desktop/laptop environment, and lacked the ability to conduct controlled experiments to provide strong evidence of differentiation. NetDiff covered a wide range of passively gathered traffic from a large ISP but likewise did not support targeted, controlled experiments. We address these limitations with Mobile Replay.

A comparison of linear and non-linear transmitter and receiver antenna array processing for interference nulling and diversity with non-zero CSI feedback delay

The performance of receiver, transmitter, and joint transmitter/receiver antenna array processing for interference nulling and diversity over fading radio links are compared and contrasted in this paper. Specific examples of linear and non-linear algorithms are considered in this context. To perform transmitter processing, a priori knowledge of the radio propagation channel is required at the transmitter. This is typically achieved by the provision of a feedback loop that supplies the transmitter with the channel state information (CSI). The impact on transmitter processing of non-zero delays in the CSI feedback loop is quantified in this paper. It is assumed that channel estimation is performed with perfect accuracy at the receiver, and that the feedback loop is error-free. It is shown that while transmitter processing out performs receiver processing at low relative Doppler frequencies, its performance degrades rapidly when the CSI feedback delay is greater than 2% of the channel coherence time. The results presented can be readily interpreted in the context of multi-user uplink and downlink in cellular systems, or collaborative signal processing among base stations.