In practice, network experiments are difficult to share and repeat due to undeclared dependencies, environment drift, and fragile imperative scripts. These problems cause experiments to silently diverge across machines and time, making results hard to verify and build upon. In this paper, we present NixNet, a framework that automates the complete experiment lifecycle—from dependency installation to resource cleanup. NixNet leverages the Nix package manager to enable reproducibility and preservation of network experiments through a virtual testbed. Testbeds are defined by a single declarative expression specifying network nodes, virtual links, link characteristics, and application scripts. By enforcing strict isolation using Linux namespaces, NixNet eliminates unintended side-effects and ensures reproducibility by pinning the entire dependency closure, including userspace binaries and input data. NixNet configures the Linux networking stack directly via syscalls, providing a lightweight yet strongly isolated testbed. We demonstrate NixNet's utility through scenarios in protocol research—including Media over QUIC streaming and LEO satellite link emulation—and provide a detailed comparison against Mininet and containerlab, showing superior scalability and isolation properties.
Benedikt Spies, Marcin Bosk, Paulo Mendes et al.· Applied Networking Research...· 0 citations
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.
Hendrik Cech, Nitinder Mohan, Jorg Ott· 1 citation
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