AI 中文总结
研究聚焦网络建模中获取可靠实验数据的问题，提出用全尺寸软件模型替代真实设备进行自然实验，基于可重复性确保可靠性，通过比较流行建模软件包得出多数符合标准，选择取决于非技术因素的结论。
AI 中文摘要
背景：在网络建模时，存在获取实验数据以验证其他模型方法的问题，即便有实验数据，也需确保其可靠性。目的：提出获取可靠实验数据的方法。方法：网络设备本质是软硬件复合体，全尺寸软件模型可视为与真实设备完全等效，真实实验可被自然实验替代，自然实验的可靠性基于其可重复性。结果：对流行的自然网络建模软件包进行了比较，按可重复研究的功能和可行性划分这些软件包。结论：多数软件包符合可重复性标准，具体解决方案的选择取决于非技术因素，如软件包的流行程度和熟悉程度。
英文摘要
Background: When we model networks, there is a problem of obtaining experimental data to verify other model approaches. And even if there are some experimental data, it is necessary to be sure of their reliability. Purpose: It is necessary to propose methods for obtaining reliable experimental data. Method: By its nature, network equipment is a software and hardware complex. Therefore, a full-scale software model can be considered completely equivalent to real equipment. And a real experiment can be replaced by a nature experiment. The reliability of a nature experiment will be based on its reproducibility. Results A comparison of popular nature network modeling packages was carried out. These packages were divided by functionality and feasibility of reproducible studies. Conclusions: Most software packages meet the reproducibility criteria. The choice of a specific solution depends on non-technical factors: popularity and knowledge of the package.
Commentsin English; in Russian
Journal refLecture Notes in Computer Science, vol 16461, 48-61 (2026)
DOI：10.1007/978-3-032-19978-2_4
