Network Performance Characterization

A reliable Internet connection is no longer a luxury but a fundamental requirement for modern life, enabling remote work, online learning, and entertainment. However, Internet performance is far from uniform, varying widely depending on geographic location and the type of connection used. These disparities can create significant inequities in access to online resources and opportunities. To address this, it is crucial to understand Internet performance at a granular, local level, uncovering patterns and identifying areas where connectivity falls short. Our research focuses on characterizing network performance across both spatial and temporal dimensions, providing the insights needed to build a more resilient and equitable Internet infrastructure.

Beyond Data Points: Regionalizing Crowdsourced Latency Measurements

Abstract. 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, such as zip codes, census tracts, or neighborhood units. However, this assumption may not be valid for two reasons: (1) crowdsourced measurements often exhibit non-uniform sampling densities within geographic areas; and (2) 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: (1) aggregate Internet performance over geographic regions; (2) overlay interpolated maps with various sampling unit choices; and (3) 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, which can better inform policy, network operations, and long-term planning decisions.

Resources

The research paper behind this paper was accepted to ACM SIGMETRICS 2025, and published in ACM POMACS in December 2024.

Citation bibtex

@article{sharma2024beyond,
  title={Beyond Data Points: Regionalizing Crowdsourced Latency Measurements},
  author={Sharma, Taveesh and Schmitt, Paul and Bronzino, Francesco and Feamster, Nick and Marwell, Nicole P},
  journal={Proceedings of the ACM on Measurement and Analysis of Computing Systems},
  volume={8},
  number={3},
  pages={1--24},
  year={2024},
  publisher={ACM New York, NY, USA}
}

Measuring the Performance of iCloud Private Relay

Abstract. Recent developments in Internet protocols and services aim to provide enhanced security and privacy for users’ traffic. Apple’s iCloud Private Relay is a premier example of this trend, introducing a well- provisioned, multi-hop architecture to protect the privacy of users’ traf- fic while minimizing the traditional drawbacks of additional network hops (e.g., latency). Announced in 2021, the service is currently in the beta stage, offering an easy and cheap privacy-enhancing alternative di- rectly integrated into Apple’s operating systems. This seamless integra- tion makes a future massive adoption of the technology very likely, calling for studies on its impact on the Internet. Indeed, the iCloud Private Relay architecture inherently introduces computational and routing overheads, possibly hampering performance. In this work, we study the service from a performance perspective, across a variety of scenarios and locations. We show that iCloud Private Relay not only reduces speed test perfor- mance (up to 10x decrease) but also negatively affects page load time and download/upload throughput in different scenarios. Interestingly, we find that the overlay routing introduced by the service may increase perfor- mance in some cases. Our results call for further investigations into the effects of a large-scale deployment of similar multi-hop privacy-enhancing architectures. For increasing the impact of our work we contribute our software and measurements to the community.

Resources

The research paper behind this paper was accepted to PAM 2023.

You can access the source code of the project as well as the collected datasets at https://github.com/marty90/icloud-private-relay-experiments

Citation bibtex

@inproceedings{trevisan2023measuring,
  title={Measuring the performance of icloud private relay},
  author={Trevisan, Martino and Drago, Idilio and Schmitt, Paul and Bronzino, Francesco},
  booktitle={International Conference on Passive and Active Network Measurement},
  pages={3--17},
  year={2023},
  organization={Springer}
}

Characterizing Service Provider Response to the COVID-19 Pandemic in the United States

Abstract. The COVID-19 pandemic has resulted in dramatic changes to the daily habits of billions of people. Users increasingly have to rely on home broadband Internet access for work, education, and other activities. These changes have resulted in corresponding changes to Internet traffic patterns. This paper aims to characterize the effects of these changes with respect to Internet service providers in the United States. We study three questions: (1) How did traffic demands change in the United States as a result of the COVID-19 pandemic?; (2) What effects have these changes had on Internet performance?; (3) How did service providers respond to these changes? We study these ques- tions using data from a diverse collection of sources. Our analysis of interconnection data for two large ISPs in the United States shows a 30–60% increase in peak traffic rates in the first quarter of 2020. In particular, we observe traffic downstream peak volumes for a major ISP increase of 13–20% while upstream peaks increased by more than 30%. Further, we observe significant variation in performance across ISPs in conjunction with the traffic volume shifts, with evident latency increases after stay-at-home orders were issued, followed by a stabilization of traffic after April. Finally, we observe that in response to changes in usage, ISPs have aggressively augmented capacity at interconnects, at more than twice the rate of normal capacity augmentation. Similarly, video conferencing applications have increased their network footprint, more than doubling their advertised IP address space.

Resources

The research paper behind this paper was accepted to PAM 2021.

Citation bibtex

@inproceedings{liu2021characterizing,
  title={Characterizing service provider response to the covid-19 pandemic in the united states},
  author={Liu, Shinan and Schmitt, Paul and Bronzino, Francesco and Feamster, Nick},
  booktitle={Passive and Active Measurement: 22nd International Conference, PAM 2021, Virtual Event, March 29--April 1, 2021, Proceedings 22},
  pages={20--38},
  year={2021},
  organization={Springer}
}