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

📰 ArXiv cs.AI

Survey on network performance modeling approaches from simulation to deep learning

advanced Published 31 Mar 2026
Action Steps
  1. Identify traditional network performance modeling approaches such as Discrete Event Simulation (DES) and analytical methods
  2. Analyze the limitations and challenges of these traditional approaches
  3. Explore the application of deep learning techniques to network performance modeling
  4. Evaluate the potential of deep learning models to improve the accuracy and efficiency of network performance prediction
Who Needs to Know This

Network engineers and researchers benefit from this survey as it provides an overview of existing approaches, while data scientists and AI engineers can apply these methods to improve network performance modeling

Key Insight

💡 Deep learning techniques can improve the accuracy and efficiency of network performance modeling

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📊 Network performance modeling: from simulation to deep learning #AI #networks

Key Takeaways

Survey on network performance modeling approaches from simulation to deep learning

Full Article

Title: From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches

Abstract:
arXiv:2603.28394v1 Announce Type: cross 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 metho
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