[2603.28394] From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches

Computer Science > Networking and Internet Architecture arXiv:2603.28394 (cs) [Submitted on 30 Mar 2026] Title:From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches Authors:Carlos Güemes-Palau, Miquel Ferriol-Galmés, Jordi Paillisse-Vilanova, Pere Barlet-Ros, Albert Cabellos-Aparicio View a PDF of the paper titled From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches, by Carlos G\"uemes-Palau and 4 other authors View PDF HTML (experimental) Abstract: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...