Problem
Research questions and friction points this paper is trying to address.
Network Performance Modeling
Discrete Event Simulation
Machine Learning
Evaluation Methodology
Taxonomy
From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches
🤖 AI Summary
This study addresses the limitations of traditional approaches in efficiency and applicability for performance prediction of data flows in wired networks. It systematically reviews the decades-long evolution of network performance modeling, encompassing discrete-event simulation, queueing theory, network calculus, machine learning, and hybrid methods. The work innovatively proposes a unified taxonomy of modeling paradigms, revealing a paradigm shift from analytical and simulation-based techniques toward data-driven deep learning. It further provides a detailed analysis of how these approaches differ in evaluation objectives, underlying assumptions, and comparability. By clarifying the strengths, limitations, and appropriate application scenarios of each methodology, this research establishes a comprehensive reference framework to guide future advances in network performance modeling.
📝 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 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Network Performance Modeling
Discrete Event Simulation
Machine Learning
Taxonomy
Hybrid Approaches
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