Starlink has become the largest commercial LEO satellite network, yet little is known about its internal queue management and bandwidth allocation mechanisms. Prior measurement studies have documented performance variations but lack the granularity to explain the underlying causes. We present the first microscopic characterization of Starlink's transmission behavior, using controlled measurements from multiple terminals to capture per-packet dynamics at microsecond precision. Our analysis uncovers several previously undocumented mechanisms. Starlink employs head-drop queuing rather than tail-drop, with capacities of approximately 1500 and 4000 packets on downlink and uplink, respectively. Bandwidth allocation is demand-driven, starting from a baseline of 100/30 Mbps on the downlink and uplink that ramps up by 3.4×/2× over 400 ms when flows sustain queue pressure. Active queue management aggressively induces packet loss to control queue occupancy, especially on the uplink. These mechanisms reset every 15 seconds during Starlink's reconfiguration cycle. We also find flow-level queuing that isolates latency between concurrent flows while coupling their loss on the downlink. These findings reveal that Starlink's queue management creates fundamentally different operating conditions than terrestrial networks.

The emerging nature of low-earth-orbiting satellite constellations has brought forth an era of global connectivity. Star-link has the potential to offer a truly global, high-performant service. An obvious question arising from this is whether Starlink can replace existing terrestrial services. In this work we describe the key findings from a detailed study of the performance of Starlink, investigating both its potential and shortcomings to act as a "global ISP".

The Starlink network from SpaceX stands out as the only commercial LEO network with over 2M+ customers and more than 4000 operational satellites. In this paper, we conduct a first-of-its-kind extensive multi-faceted analysis of Starlink performance leveraging several measurement sources. First, based on 19.2M crowdsourced M-Lab speed tests from 34 countries since 2021, we analyze Starlink global performance relative to terrestrial cellular networks. Second, we examine Starlink's ability to support real-time latency and bandwidth-critical applications by analyzing the performance of (i) Zoom conferencing, and (ii) Luna cloud gaming, comparing it to 5G and fiber. Third, we perform measurements from Starlink-enabled RIPE Atlas probes to shed light on the last-mile access and other factors affecting its performance.Finally, we conduct controlled experiments from Starlink dishes in two countries and analyze the impact of globally synchronized "15-second reconfiguration intervals'' of the satellite links that cause substantial latency and throughput variations. Our unique analysis paints the most comprehensive picture of Starlink's global and last-mile performance to date.

Emerging Remote Piloting (RP) operations of electrified Unmanned Aerial Vehicles (UAVs) demand low-latency and high-quality video delivery to conduct safe operations in the low-altitude airspace. Although cellular networks are one of the prominent candidates to provide connectivity for such operations, their ground-centric nature limits their capabilities in achieving seamless and reliable aerial connectivity. In this paper, we study the feasibility of supporting RP operations with low latency and high-quality video delivery over commercial cellular networks. By setting up an adaptive bitrate video transmission pipeline with the Google Congestion Control (GCC) and Self-Clocked Rate Adaptation for Multimedia (SCReAM) Congestion Control (CC) algorithms, we analyze the video delivery performance for the RP application requirements and compare the performance of GCC and SCReAM against constant bitrate video delivery. Our results show that low-latency video delivery with < 300 ms playback latency between full-HD and 4K resolution can be maintained up to about 95% of the time in the air. While static bitrate video delivery outperforms adaptive streaming in urban location with abundant link capacity, the latter becomes advantageous in rural locations, where the link capacity is affected by fluctuations. Although the study's findings highlight the capabilities of cellular networks in delivering low-latency video for a safety-critical aerial service, we also discuss the potential improvements and future research challenges for enabling safe operations and meeting the service requirements using cellular networks. We release our collected traces and the video transmission pipeline as open-source to facilitate research in this field.

Distributed systems are difficult to debug and understand. A key reason for this is distributed state, which is not easily accessible and must be pieced together from the states of the individual nodes in the system.