Taveesh Sharma

2024

1. PRE-PRINT

   From Point Data to Geographic Boundaries: 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.