Network performance modeling is a field that predates early computer networks and the beginning of the Internet. It aims to predict the traffic performance of packet flows in a given network. Its applications range from network planning and troubleshooting to feeding information to network controllers for configuration optimization. Traditional network performance modeling has relied heavily on Discrete Event Simulation (DES) and analytical methods grounded in mathematical theories such as Queuing Theory and Network Calculus. However, as of late, we have observed a paradigm shift, with attempts to obtain efficient Parallel DES, the surge of Machine Learning models, and their integration with other methodologies in hybrid approaches. This has resulted in a great variety of modeling approaches, each with its strengths and often tailored to specific scenarios or requirements. In this paper, we comprehensively survey the relevant network performance modeling approaches for wired networks over the last decades. With this understanding, we also define a taxonomy of approaches, summarizing our understanding of the state-of-the-art and how both technology and the concerns of the research community evolve over time. Finally, we also consider how these models are evaluated, how their different nature results in different evaluation requirements and goals, and how this may complicate their comparison.

翻译：网络性能建模是一个早于早期计算机网络和互联网诞生的研究领域，旨在预测给定网络中数据包流量的性能。其应用范围涵盖网络规划、故障排除，以及向网络控制器提供信息以优化配置。传统网络性能建模主要依赖于离散事件仿真（DES）以及基于排队论、网络演算等数学理论的分析方法。然而，近年来我们观察到一种范式转变：高效并行DES的尝试、机器学习模型的涌现，以及它们与其它方法在混合方案中的融合。这导致了丰富多样的建模方法，每种方法各有优势，并通常针对特定场景或需求而定制。本文全面综述了过去几十年来有线网络相关性能建模方法。基于此理解，我们定义了一种方法分类法，总结了我们对当前技术发展水平的认知，以及技术本身和研究界关注点如何随时间演变。最后，我们还探讨了这些模型如何被评估，它们的不同性质如何导致不同的评估要求和目标，以及这如何加剧了它们之间的比较难度。