Implementation and Usage¶

We highlight the salient aspects of STARRY NET’s implementation and usage in this section.

Framework implementation. STARRY NET’s observer is implemented as a combination of a crawler based on Scrapy [27], together with a MySQL-based data store. We implement STARRY NET’s synchronizor based on SkyField [29], an astronomy library that supports the calculation of high precision research-grade positions for satellites. STARRY NET’s orchestrator is implemented upon Docker [6] and it spans the emulated constellation across multiple machines. We use OpenvSwitch [19] to emulate and configure links, and use tc [67] to dynamically set artificial network conditions according to the numeric results calculated by STARRY NET’s synchronizer. Specifically, we optimized the link management module in tc to satisfy the requirement of light-weight state update. To accomplish flexibility, S TAR RY N ET ’s abstraction is implemented as a combination of a lib-STARRY NET library and a collection of shell commands. Collectively, the core components of STARRY NET are implemented in about 6500 lines of Python codes and scripts.

框架实现

观测器模块实现

数据采集层：基于Scrapy框架[27]构建分布式爬虫系统，支持多源异构数据（星座监管、轨道参数、地面站拓扑等）的自动化采集与清洗。
存储架构：采用MySQL数据库集群实现三层数据存储模型：
- 卫星元数据表（satellite_metadata）存储NORAD编号、TLE数据等
- 地面设施拓扑图（ground_station_graph）采用邻接表结构记录站点间连接关系
- 用户行为日志（user_activity_log）通过分区表优化时序数据查询效率

同步器模块实现

天文计算引擎：集成SkyField库[29]实现高精度星历解算，关键算法包括：
- SGP4/SDP4轨道模型解析器（误差 <1km）
- 基于JPL DE421星历的星间可视性矩阵生成
动态参数生成：开发混合建模工具链，将Walker星座参数与实测数据融合，输出时变网络状态参数（传播时延、链路通断事件等）

协调器模块实现

容器化仿真：基于Docker[6]构建轻量化虚拟节点，单物理机支持200+容器实例
- 定制化基础镜像（starrynet-base）集成网络协议栈与资源监控代理
跨机拓扑管理：采用OpenvSwitch[19]实现VLAN级虚拟链路隔离，关键优化包括：
- 分布式链路状态数据库（LSDB）实现秒级拓扑同步
- 基于增量更新的BGP-LS协议优化跨机路由收敛
动态QoS控制：深度定制Linux tc工具[67]，创新实现：
- 微秒级时延抖动注入（netem模块增强）
- 带宽动态调整API支持滑动时间窗配置

统一抽象层实现

开发者接口：
- lib-starrynet + Shell CLI
- 感觉完全类比mininet的形式即可 :))

Framework usage. We illustrate the usage of STARRY NET with a concrete example as plotted in Figure 4: a researcher wants to evaluate a novel geo-location-based routing mechanism based on [63], under the Starlink constellation. In particular, this experiment can be conducted with STARRY NET in three steps. First, leveraging STARRY NET’s APIs, the researcher writes a user-defined experimental program (Figure 4a) for test. In this example, we show a geo-routing policy similar to [63], which runs on each satellite, and forwards received packets to the adjacent satellite that is the geographically closest to the destination. Second, the researcher prepares a set of manifest files describing the constellation configurations, e.g., orbital information and ground station distribution (Figure 4b). Finally, the researcher runs a batch of shell commands exposed by STARRY NET to load manifest files (e.g., starlink.json and gs.json), create experimental environment (e.g., “sl_con”) on multiple machines, configure network parameters (e.g., uplink/downlink capacity), and run the user-specific program on emulated satellites (Figure 4c).

alt text

框架使用。我们通过一个具体的例子来说明如何使用STARRY NET，如图4所示：

一位研究人员希望基于文献[63]评估一种新型的基于地理位置的路由机制，该实验在Starlink星座下进行。具体而言，使用STARRY NET可以通过三个步骤来完成此实验。

首先，研究人员利用STARRY NET的API编写用户定义的实验程序（图4a）进行测试。在此示例中，我们展示了一个类似于文献[63]的地理路由策略，该策略在每颗卫星上运行，并将接收到的数据包转发给地理位置最接近目标的相邻卫星。

其次，研究人员准备一组描述星座配置的清单文件，如轨道信息和地面站分布（图4b）。

最后，研究人员运行一批由STARRY NET提供的Shell命令，加载清单文件（例如，starlink.json和gs.json），在多个机器上创建实验环境（如“sl_con”），配置网络参数（如上行/下行容量），并在模拟卫星上运行用户特定的程序（图4c）。