Previous literature had accentuated the importance of close ground infrastructure (Ground Stations and Points of presence) on the network performance of Starlink. In order to further investigate this relation, a new method was defined, based on IPv4traceroute, to identify the PoP associated with a Starlink user. This method has been evaluated for 95 RIPE Atlas probes connected to the Starlink network and the results have been mapped in an interactive web-tool. Using this data, a strong correlation between latency and proximity to ground infrastructure was verified.

We present a machine learning framework aimed at forecasting Starlink (LEO satellite) network performance at fine spatiotemporal resolution. Our approach combines MLab crowdsourced measurements, weather and forecast features, and dynamic satellite density to predict packet loss, jitter, latency, and throughput. We introduce a composite Weather Index and real-time satellite density per location, and train robust ensemble models with anomaly filtering and median aggregation. Our best models achieve good predictive results with less than 17 ms for latency, and 35 Mbps for throughput. Latency is reliably predictable with meteorological and satellite context, while packet loss and jitter remain challenging. Predictions are limited to periods close to the training data and our results establish a reproducible baseline for short-term, weather-aware Starlink network forecasting.

The rise of Low-Earth-Orbit (LEO) satellite networks, such as Starlink, has transformed global connectivity, enabling high-speed internet access in previously underserved regions. However, existing research lacks a unified framework to evaluate and compare the performance of LEO ISPs against terrestrial alternatives using heterogeneous measurement datasets. In this work, we present a methodology for harmonizing and standardizing passive internet measurements from M-Lab’s NDT7 and Cloudflare’s AIM datasets, implemented in the form of the Global Telemetry System. These sources are integrated through schema unification, filtering, and normalization to produce a reproducible and geographically comprehensive telemetry dataset. We introduce a server-based filtering approach to mitigate geographic and routing biases, and we evaluate aggregation methods to align measurement distributions across datasets. Our results demonstrate the dataset integration methodology preserves key distributional properties, enabling fair and statistically consistent merging of measurements from the two sources. This work represents a first step toward a scalable and extensible telemetry infrastructure for assessing next-generation global internet services.

Low Earth Orbit (LEO) satellite constellations, particularly SpaceX’s Starlink, have quickly gained popularity and have become a viable alternative to traditional terrestrial Internet Service Providers (ISPs) in recent years. However, due to their novelty and unique architecture, research into their performance is limited, especially one comparing LEO and terrestrial internet. This paper will demonstrate how user-initiated active measurements can be used to both gather new data about LEO internet and assess and compare the performance of individual networks. First, performance metrics that reflect typical internet usage scenarios, such as web browsing and video streaming, are chosen. Next, a test suite is developed to collect data about one’s network. It also augments this data with location information to aid in later comparison. This data can then be used for comparisons between individual networks as well as in further research. The last step integrates the suit developed into a web based platform that aims to provide a wide variety of information about Starlink’s performance, architecture and allow users to gauge the potential benefits of transitioning from terrestrial to LEO internet could provide them.