Publications

2024

1. PRE-PRINT

   Beyond Data Points: Regionalizing Crowdsourced Latency Measurements

   2024

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   Despite significant investments in access network infrastructure, universal access to high-quality Internet connectivity remains a challenge. Policymakers often rely on large-scale, crowdsourced measurement datasets to assess the distribution of access network performance across geographic areas. These decisions typically rest on the assumption that Internet performance is uniformly distributed within predefined social boundaries. However, this assumption may not be valid for two reasons: crowdsourced measurements often exhibit non-uniform sampling densities within geographic areas; and predefined social boundaries may not align with the actual boundaries of Internet infrastructure. In this paper, we present a spatial analysis on crowdsourced datasets for constructing stable boundaries for sampling Internet performance. We hypothesize that greater stability in sampling boundaries will reflect the true nature of Internet performance disparities than misleading patterns observed as a result of data sampling variations. We apply and evaluate a series of statistical techniques to: aggregate Internet performance over geographic regions; overlay interpolated maps with various sampling unit choices; and spatially cluster boundary units to identify contiguous areas with similar performance characteristics. We assess the effectiveness of the techniques we apply by comparing the similarity of the resulting boundaries for monthly samples drawn from the dataset. Our evaluation shows that the combination of techniques we apply achieves higher similarity compared to directly calculating central measures of network metrics over census tracts or neighborhood boundaries. These findings underscore the important role of spatial modeling in accurately assessing and optimizing the distribution of Internet performance, to inform policy, network operations, and long-term planning decisions.

2023

1. IMC

   Estimating WebRTC Video QoE Metrics Without Using Application Headers

   Taveesh Sharma, Tarun Mangla, Arpit Gupta, Junchen Jiang, and Nick Feamster

   In Proceedings of the 2023 ACM Internet Measurement Conference, 2023

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   The increased use of video conferencing applications (VCAs) has made it critical to understand and support end-user quality of experience (QoE) by all stakeholders in the VCA ecosystem, especially network operators, who typically do not have direct access to client software. Existing VCA QoE estimation methods use passive measurements of application-level Real-time Transport Protocol (RTP) headers. However, a network operator does not always have access to RTP headers in all cases, particularly when VCAs use custom RTP protocols (e.g., Zoom) or due to system constraints (e.g., legacy measurement systems). Given this challenge, this paper considers the use of more standard features in the network traffic, namely, IP and UDP headers, to provide per-second estimates of key VCA QoE metrics such as frames rate and video resolution. We develop a method that uses machine learning with a combination of flow statistics (e.g., throughput) and features derived based on the mechanisms used by the VCAs to fragment video frames into packets. We evaluate our method for three prevalent VCAs running over WebRTC: Google Meet, Microsoft Teams, and Cisco Webex. Our evaluation consists of 54,696 seconds of VCA data collected from both (1), controlled in-lab network conditions, and (2) real-world networks from 15 households. We show that the ML-based approach yields similar accuracy compared to the RTP-based methods, despite using only IP/UDP data. For instance, we can estimate FPS within 2 FPS for up to 83.05% of one-second intervals in the real-world data, which is only 1.76% lower than using the application-level RTP headers.

2. TPRC

   A First Look at the Spatial and Temporal Variability of Internet Performance Data in Hyperlocal Geographies

   Taveesh Sharma, Jonatas Marques, Nick Feamster, and Nicole P Marwell

   The annual Research Conference on Communications, Information and Internet Policy, 2023

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   Measuring Internet access network performance has been a persistent challenge for researchers and policymakers alike. Unfortunately, existing speed test datasets typically lack comprehensive data across both space and time. Specifically, our past work has highlighted that tools like Ookla’s Speed Test and Measurement Lab’s NDT rely heavily on convenience samples (user-initiated tests from self-selected participants), resulting in a sample that may not generalize across either time or geography. Our ongoing research seeks to address these issues by developing innovative sampling methods and statistical models to provide a more holistic view of Inter- net performance. Initial findings, focusing on end-to-end latency across hyper-local regions within a single large city in the United States (Chicago, Illinois), reveal that spatial proximity often does not correlate with simultaneous performance anomalies. These insights underscore the need for advanced methods to generalize Internet performance data across time and space. Improved methods can ultimately en- able a better understanding of the effects of infrastructure investments on the evolution of Internet performance.

2022

1. HAISA

   PowerQoPE: A Personal Quality of Internet Protection and Experience Configurator

   Enock Samuel Mbewe, Taveesh Sharma, and Josiah Chavula

   In Proceedings of IFIP International Symposium on Human Aspects of Information Security & Assurance (HAISA 2022), 6-7 July 2022, Mytilene, (Greece), 2022

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   Security configuration remains obscure for many Internet users, especially those with limited computing skills. This obscurity exposes such users to various Internet attacks. Recently, there has been an increase in cyberattacks targeted at individuals due to the remote workforce imposed by the COVID 19 pandemic. These attacks have exposed the inefficiencies of the non-human-centric implementation of Internet security mechanisms and protocols. Security research usually positions users as the weakest link in the security ecosystem, making system and protocol developers exclude the users in the development process. This stereotypical approach has negatively affected users’ security uptake. Mostly, security systems are not comprehensible for an average user, negatively affecting performance and Quality of Experience. This causes the users to shun using security mechanisms. Building on human-centric cybersecurity research, we present a tool that aids in configuring Internet Quality of protection and Experience (referred to as PowerQoPE in this paper). We describe its architecture and design methodology and finally present evaluation results. Preliminary evaluation results show that user-centric and data-driven approaches in the design of Internet security systems improve users’ Quality of Experience. The controlled experiment results show that users are not really stupid; they know what they want and that given proper security configuration platforms with proper framing of components and information, they can make optimal security decisions.

2. SAICSIT

   Investigating the Usability and Quality of Experience of Mobile Video-Conferencing Apps Among Bandwidth-Constrained Users in South Africa

   Dominique Oosthuizen, Taveesh Sharma, Josiah Chavula, and Melissa Densmore

   In Proceedings of 43rd Conference of the South African Institute of Computer Scientists and Information Technologists, 2022

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   During the COVID-19 pandemic and mandated global lockdowns, people and businesses started the extensive use of video-conferencing applications for staying connected. This surge in demand and the usability of video-conferencing services has been severely overlooked in developing countries like South Africa, where one-third of adults rely on mo- bile devices to access the internet, and the per-gigabyte data cost is among the highest in Africa. Considering these numbers, we conduct a two-pronged study where 1) we measure data consumption of different Android apps through data measurement experiments and 2) we conduct interviews and usability assessments with bandwidth-constrained users to better understand the usability and Quality of Experience (QoE) of mobile video-conferencing apps. Usability is the degree to which specified users can use a product to achieve specified goals. In contrast, QoE measures the subjective perception of the quality of an application and the level of delight or annoyance with a service. The key benefit of this study will be to inform organisations that seek to be inclusive about these tools’ relative usability by letting them know about the factors influencing users’ QoE.

2021

1. SATNAC

   Topology-Aware Measurement Scheduling Strategies in Low Resource Networks

   Taveesh Sharma, and Josiah Chavula

   In Proceedings of the Southern African Telecommunication Networks and Applications Conference (SATNAC), 2021

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   Community networks have been proposed by many networking experts and researchers as a way to bridge the connectivity gaps in rural and remote areas of the world. Many community networks are built with low-capacity computing devices and low-capacity links. Such community networks are examples of low resource networks. The design and implementation of computer networks using limited hardware and software resources has been studied extensively in the past, but scheduling strategies for conducting measurements on these networks remains an important area to be explored. In this study, the design of a Quality of Service monitoring system is proposed, focusing on performance of scheduling of network measurement jobs in different topologies of a low-resource network. Our results show that a graph colouring algorithm (AOSD) that arranges network measurement jobs in ascending order of their number of conflicts performs better than other scheduling algorithms like Round Robin (RR) and Earliest Deadline First (EDF).

2020

1. COMPASS

   Investigating Measurement Scheduling Strategies in Low Resource Networks (Poster)

   Taveesh Sharma, and Josiah Chavula

   In ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS), 2020

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   Community networks have been proposed by many networking experts and researchers as a way to bridge connectivity gaps in rural and remote areas of the world. Many community networks are built with low-capacity computing devices and low-capacity links. Such community networks are examples of low resource networks. The design and implementation of computer networks using limited hardware and software resources has been studied extensively in the past, but scheduling strategies for conducting measurements on these networks remains an important area to be explored. In this study, the design of a Quality of Service monitoring system is proposed, focusing on performance of scheduling of network measurement jobs in a low-resource network. In this paper, we present a testbed for conducting performance evaluation of two measurement scheduling algorithms and present an analysis of trends in their performance with varying experiment profiles.