Fabián Bustamante’s research while affiliated with Northwestern University and other places

The Domain Name System (DNS) is both a key determinant of users' quality of experience (QoE) and privy to their tastes, preferences, and even the devices they own. Growing concern about user privacy and QoE has brought a number of alternative DNS services, from public DNS to encrypted and Oblivious DNS. While offering valuable features, these DNS variants are operated by a handful of providers, reinforcing a trend towards centralization that has raised concerns about privacy, competition, resilience and Web QoE. The goal of this work is to let users take advantage of third-party DNS services, without sacrificing privacy or performance. We follow Wheeler's advice, adding another level of indirection with an end-system DNS resolver, Onoma, that improves privacy, avoiding DNS-based user-reidentification by inserting and sharding requests across resolvers, and improves performance by running resolution races among resolvers and reinstating the client-resolver proximity assumption content delivery networks rely on. As our evaluation shows, while there may not be an ideal service for all clients in all places, Onoma dynamically finds the best service for any given location.

Advances in cloud computing have simplified the way that both software development and testing are performed. This is not true for battery testing for which state of the art test-beds simply consist of one phone attached to a power meter. These test-beds have limited resources, access, and are overall hard to maintain; for these reasons, they often sit idle with no experiment to run. In this paper, we propose to share existing battery testbeds and transform them into vantage points of BatteryLab, a power monitoring platform offering heterogeneous devices and testing conditions. We have achieved this vision with a combination of hardware and software which allow to augment existing battery test-beds with remote capabilities. BatteryLab currently counts three vantage points, one in Europe and two in the US, hosting three Android devices and one iPhone 7. We benchmark BatteryLab with respect to the accuracy of its battery readings, system performance, and platform heterogeneity. Next, we demonstrate how measurements can be run atop of BatteryLab by developing the "Web Power Monitor" (WPM), a tool which can measure website power consumption at scale. We released WPM and used it to report on the energy consumption of Alexa's top 1,000 websites across 3 locations and 4 devices (both Android and iOS).

Advances in cloud computing have simplified the way that both software development and testing are performed. This is not true for battery testing for which state of the art test-beds simply consist of one phone attached to a power meter. These test-beds have limited resources, access, and are overall hard to maintain; for these reasons, they often sit idle with no experiment to run. In this paper, we propose to share existing battery testbeds and transform them into vantage points of BatteryLab, a power monitoring platform offering heterogeneous devices and testing conditions. We have achieved this vision with a combination of hardware and software which allow to augment existing battery test-beds with remote capabilities. BatteryLab currently counts three vantage points, one in Europe and two in the US, hosting three Android devices and one iPhone 7. We benchmark BatteryLab with respect to the accuracy of its battery readings, system performance, and platform heterogeneity. Next, we demonstrate how measurements can be run atop of BatteryLab by developing the “Web Power Monitor” (WPM), a tool which can measure website power consumption at scale. We released WPM and used it to report on the energy consumption of Alexa’s top 1,000 websites across 3 locations and 4 devices (both Android and iOS).KeywordsBatteryTest-bedPerformanceAndroidiOS

Support for things roaming internationally has become critical for Internet of Things (IoT) verticals, from connected cars to smart meters and wearables, and explains the commercial success of Machine-to-Machine (M2M) platforms. We analyze IoT verticals operating with connectivity via IoT SIMs, and present the first large-scale study of commercially deployed IoT SIMs for energy meters. We also present the first characterization of an operational M2M platform and the first analysis of the rather opaque associated ecosystem. For operators, the exponential growth of IoT has meant increased stress on the infrastructure shared with traditional roaming traffic. Our analysis quantifies the adoption of roaming by M2M platforms and the impact they have on the underlying visited Mobile Network Operators (MNOs). To manage the impact of massive deployments of device operating with an IoT SIM, operators must be able to distinguish between the latter and traditional inbound roamers. We build a comprehensive dataset capturing the device population of a large European MNO over three weeks. With this, we propose and validate a classification approach that can allow operators to distinguish inbound roaming IoT devices.

The IPX Network interconnects about 800 Mobile Network Operators (MNOs) worldwide and a range of other service providers (such as cloud and content providers). It forms the core that enables global data roaming while supporting emerging applications, from VoLTE and video streaming to IoT verticals. This paper presents the first characterization of this, so-far opaque, IPX ecosystem and a first-of-its-kind in-depth analysis of ann IPX Provider (IPX-P). The IPX Network is a private network formed by a small set of tightly interconnected IPX-Ps. We analyze an operational dataset from a large IPX-P that includes BGP data as well as statistics from signaling. We shed light on the structure of the IPX Network as well as on the temporal, structural and geographic features of the IPX traffic. Our results are a first step in understanding the IPX Network at its core, key to fully understand the global mobile Internet.

There has been a growing interest in measuring and optimizing the power efficiency of mobile apps. Traditional power evaluations rely either on inaccurate software-based solutions or on ad-hoc testbeds composed of a power meter and a mobile device. This demonstration presents BatteryLab, our solution to share existing battery testing setups to build a distributed platform for battery measurements. Our vision is to transform independent battery testing setups into vantage points of a planetary-scale measurement platform offering heterogeneous devices and testing conditions. We demonstrate BatteryLab functionalities by investigating the energy efficiency of popular websites when loaded via both Android and iOS browsers. Our demonstration is also live at https://batterylab.dev/.

This is a report on the Workshop on Tracking Quality of Experience in the Internet, held at Princeton, October 21--22, 2015, jointly sponsored by the National Science Foundation and the Federal Communication Commission. The term Quality of Experience (QoE) describes a user's subjective assessment of their experience when using a particular application. In the past, network engineers have typically focused on Quality of Service (QoS): performance metrics such as throughput, delay and jitter, packet loss, and the like. Yet, performance as measured by QoS parameters only matters if it affects the experience of users, as they attempt to use a particular application. Ultimately, the user's experience is determined by QoE impairments (e.g., rebuffering). Although QoE and QoS are related---for example, a video rebuffering event may be caused by high packet-loss rate---QoE metrics ultimately affect a user's experience. Identifying the causes of QoE impairments is complex, since the impairments may arise in one or another region of the network, in the home network, on the user's device, in servers that are part of the application, or in supporting services such as the DNS. Additionally, metrics for QoE continue to evolve, as do the methods for relating QoE impairments to underlying causes that could be measurable using standard network measurement techniques. Finally, as the capabilities of the underlying network infrastructure continues to evolve, researchers should also consider how to design infrastructure and tools can best support measurements that can better identify the locations and causes of QoE impairments. The workshop's aim was to understand the current state of QoE research and to contemplate a community agenda to integrate ongoing threads of QoE research into a collaboration. This summary report describes the topics discussed and summarize the key points of the discussion. Materials related to the workshop are available at http://aqualab.cs.northwestern.edu/NSFWorkshop-InternetQoE.