OpenSN: An Open Source Library for Emulating LEO Satellite Networks

IF 5.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2025-06-02 DOI:10.1109/TPDS.2025.3575920

Wenhao Lu;Zhiyuan Wang;Hefan Zhang;Shan Zhang;Hongbin Luo

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引用次数: 0

Abstract

Low-earth-orbit (LEO) satellite constellations (e.g., Starlink) are becoming a necessary component of future Internet. There have been increasing studies on LEO satellite networking. It is a crucial problem how to evaluate these studies in a systematic and reproducible manner. In this paper, we present OpenSN, i.e., an open source library for emulating large-scale satellite network (SN). Different from Mininet-based SN emulators (e.g., LeoEM), OpenSN adopts container-based virtualization, thus allows for running distributed routing software on each node, and can achieve horizontal scalability via flexible multi-machine extension. Compared to other container-based SN emulators (e.g., StarryNet), OpenSN streamlines the interaction with Docker command line interface and significantly reduces unnecessary operations of creating virtual links. These modifications improve emulation efficiency and vertical scalability on a single machine. Furthermore, OpenSN separates user-defined configuration from container network management via a Key-Value Database that records the necessary information for SN emulation. Such a separation architecture enhances the function extensibility. To sum up, OpenSN exhibits advantages in efficiency, scalability, and extensibility, thus is a valuable open source library that empowers research on LEO satellite networking. Experiment results show that OpenSN constructs mega-constellations 5X-10X faster than StarryNet, and updates link state 2X-4X faster than LeoEM. We also verify the scalability of OpenSN by successfully emulating the five-shell Starlink constellation with a total of 4408 satellites.

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openn：一个用于模拟LEO卫星网络的开源库

低地球轨道（LEO）卫星星座（例如Starlink）正在成为未来互联网的必要组成部分。对低轨道卫星组网的研究越来越多。如何对这些研究进行系统的、可重复的评价是一个关键问题。在本文中，我们提出了OpenSN，即一个用于模拟大规模卫星网络（SN）的开源库。与基于miniet的SN模拟器（如LeoEM）不同，OpenSN采用基于容器的虚拟化，因此可以在每个节点上运行分布式路由软件，并可以通过灵活的多机扩展实现水平扩展。与其他基于容器的SN模拟器（如StarryNet）相比，OpenSN简化了与Docker命令行接口的交互，大大减少了不必要的创建虚拟链接的操作。这些修改提高了仿真效率和单台机器上的垂直可伸缩性。此外，OpenSN通过一个Key-Value数据库将用户定义的配置与容器网络管理分离，该数据库记录了SN仿真所需的信息。这种分离体系结构增强了功能的可扩展性。总而言之，OpenSN在效率、可伸缩性和可扩展性方面具有优势，因此是一个有价值的开源库，可以为LEO卫星网络的研究提供支持。实验结果表明，openn构建超级星座的速度比StarryNet快5 -10倍，链路状态更新速度比LeoEM快2 -4倍。我们还通过成功模拟共有4408颗卫星的五壳Starlink星座，验证了OpenSN的可扩展性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Parallel and Distributed Systems 工程技术-工程：电子与电气

CiteScore

11.00

自引率

9.40%

发文量

281

审稿时长

5.6 months

期刊介绍： IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.