System Support for Bandwidth Management and Content Adaptation in Internet Applications

This paper describes the implementation and evaluation of an operating system module, the Congestion Manager (CM), which provides integrated network flow management and exports a convenient programming interface that allows applications to be notified of, and adapt to, changing network conditions. We describe the API by which applications interface with the CM, and the architectural considerations that factored into the design. To evaluate the architecture and API, we describe our implementations of TCP; a streaming layered audio/video application; and an interactive audio application using the CM, and show that they achieve adaptive behavior without incurring much end-system overhead. All flows including TCP benefit from the sharing of congestion information, and applications are able to incorporate new functionality such as congestion control and adaptive behavior.Comment: 14 pages, appeared in OSDI 200

Congestion-Aware Scalable Video Streaming

Postprin

Real-time Audio-Visual Media Transport over QUIC

We consider the problem of how to transport low-latency, interactive, real-time traffic over QUIC. This is needed to support applications like WebRTC, but difficult to support due to the reliable, unframed, nature of QUIC streams. We review the needs of low-latency real-time applications and how they have been supported in previous protocols, then propose a minimal set of extensions to QUIC to provide such support. Compared to a raw datagram service, our extensions provide meaningful support for partially reliable and real-time flows, in a backwards compatible manner

Undermining User Privacy on Mobile Devices Using AI

Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users. This is because applications leave distinct footprints in the processor, which can be used by malware to infer user activities. In this work, we show that these inference attacks are considerably more practical when combined with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with Deep Learning methods including Convolutional Neural Networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zeropermission App in well under a minute. The App thereby detects running applications with an accuracy of 98% and reveals opened websites and streaming videos by monitoring the LLC for at most 6 seconds. This is possible, since Deep Learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics such as random line replacement policies. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to implement and execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users

Robust P2P Live Streaming

Projecte fet en col.laboració amb la Fundació i2CATThe provisioning of robust real-time communication services (voice, video, etc.) or media contents through the Internet in a distributed manner is an important challenge, which will strongly influence in current and future Internet evolution. Aware of this, we are developing a project named Trilogy leaded by the i2CAT Foundation, which has as main pillar the study, development and evaluation of Peer-to-Peer (P2P) Live streaming architectures for the distribution of high-quality media contents. In this context, this work concretely covers media coding aspects and proposes the use of Multiple Description Coding (MDC) as a flexible solution for providing robust and scalable live streaming over P2P networks. This work describes current state of the art in media coding techniques and P2P streaming architectures, presents the implemented prototype as well as its simulation and validation results

Survey of End-to-End Mobile Network Measurement Testbeds, Tools, and Services

Mobile (cellular) networks enable innovation, but can also stifle it and lead to user frustration when network performance falls below expectations. As mobile networks become the predominant method of Internet access, developer, research, network operator, and regulatory communities have taken an increased interest in measuring end-to-end mobile network performance to, among other goals, minimize negative impact on application responsiveness. In this survey we examine current approaches to end-to-end mobile network performance measurement, diagnosis, and application prototyping. We compare available tools and their shortcomings with respect to the needs of researchers, developers, regulators, and the public. We intend for this survey to provide a comprehensive view of currently active efforts and some auspicious directions for future work in mobile network measurement and mobile application performance evaluation.Comment: Submitted to IEEE Communications Surveys and Tutorials. arXiv does not format the URL references correctly. For a correctly formatted version of this paper go to http://www.cs.montana.edu/mwittie/publications/Goel14Survey.pd

Efficient and Effective Schemes for Streaming Media Delivery

The rapid expansion of the Internet and the increasingly wide deployment of wireless networks provide opportunities to deliver streaming media content to users at anywhere, anytime. To ensure good user experience, it is important to battle adversary effects, such as delay, loss and jitter. In this thesis, we first study efficient loss recovery schemes, which require pure XOR operations. In particular, we propose a novel scheme capable of recovering up to 3 packet losses, and it has the lowest complexity among all known schemes. We also propose an efficient algorithm for array codes decoding, which achieves significant throughput gain and energy savings over conventional codes. We believe these schemes are applicable to streaming applications, especially in wireless environments. We then study quality adaptation schemes for client buffer management. Our control-theoretic approach results in an efficient online rate control algorithm with analytically tractable performance. Extensive experimental results show that three goals are achieved: fast startup, continuous playback in the face of severe congestion, and maximal quality and smoothness over the entire streaming session. The scheme is later extended to streaming with limited quality levels, which is then directly applicable to existing systems