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Delivering videos under less-than-ideal network conditions without compromising end-users' quality of experiences is a hard problem. Virtually all prior work follow a piecemeal approach---either "tweaking" the fully reliable transport layer or making the client "smarter." We propose VOXEL, a cross-layer optimization system for video streaming. We use VOXEL to demonstrate how to combine application-provided "insights" with a partially reliable protocol for optimizing video streaming. To this end, we present a novel ABR algorithm that explicitly trades off losses for improving end-users' video-watching experiences. VOXEL is fully compatible with DASH, and backward-compatible with VOXEL-unaware servers and clients. In our experiments emulating a wide range of network conditions, VOXEL outperforms the state-of-the-art: We stream videos in the 90th-percentile with up to 97% less rebuffering than the state-of-the-art without sacrificing visual fidelity. We also demonstrate the benefits of VOXEL for small-buffer regimes like the emerging use case of low-latency and live streaming. In a survey of 54 real users, 84% of the participants indicated that they prefer videos streamed using VOXEL compared to the state-of-the-art. 

There is a rich body of literature on measuring and optimizing nearly every aspect of the web, including characterizing the structure and content of web pages, devising new techniques to load pages quickly, and evaluating such techniques. Virtually all of this prior work used a single page, namely the landing page (i.e., root document, "/"), of each web site as the representative of all pages on that site. In this paper, we characterize the differences between landing and internal (i.e., non-root) pages of 1000 web sites to demonstrate that the structure and content of internal pages differ substantially from those of landing pages, as well as from one another. We review more than a hundred studies published at top-tier networking conferences between 2015 and 2019, and highlight how, in light of these differences, the insights and claims of nearly two-thirds of the relevant studies would need to be revised for them to apply to internal pages. Going forward, we urge the networking community to include internal pages for measuring and optimizing the web. This recommendation, however, poses a non-trivial challenge: How do we select a set of representative internal web pages from a web site? To address the challenge, we have developed Hispar, a "top list" of 100,000 pages updated weekly comprising both the landing pages and internal pages of around 2000 web sites. We make Hispar and the tools to recreate or customize it publicly available. 

Internet of Things (IoT) devices are becoming increasingly popular and offer a wide range of services and functionality to their users. However, there are significant privacy and security risks associated with these devices. IoT devices can infringe users' privacy by ex-filtrating their private information to third parties, often without their knowledge. In this work we investigate the possibility to identify IoT devices and their location in an Internet Service Provider's network. By analyzing data from a large Internet Service Provider (ISP), we show that it is possible to recognize specific IoT devices, their vendors, and sometimes even their specific model, and to infer their location in the network. This is possible even with sparsely sampled flow data that are often the only datasets readily available at an ISP. We evaluate our proposed methodology to infer IoT devices at subscriber lines of a large ISP. Given ground truth information on IoT devices location and models, we were able to detect more than 77% of the studied IoT devices from sampled flow data in the wild. 

Consumer Internet of Things (IoT) devices are extremely popular, providing users with rich and diverse functionalities, from voice assistants to home appliances. These functionalities often come with significant privacy and security risks, with notable recent large-scale coordinated global attacks disrupting large service providers. Thus, an important first step to address these risks is to know what IoT devices are where in a network. While some limited solutions exist, a key question is whether device discovery can be done by Internet service providers that only see sampled flow statistics. In particular, it is challenging for an ISP to efficiently and effectively track and trace activity from IoT devices deployed by its millions of subscribers---all with sampled network data. In this paper, we develop and evaluate a scalable methodology to accurately detect and monitor IoT devices at subscriber lines with limited, highly sampled data in-the-wild. Our findings indicate that millions of IoT devices are detectable and identifiable within hours, both at a major ISP as well as an IXP, using passive, sparsely sampled network flow headers. Our methodology is able to detect devices from more than 77% of the studied IoT manufacturers, including popular devices such as smart speakers. While our methodology is effective for providing network analytics, it also highlights significant privacy consequences.