Literature Database Entry

spath2024sim2hw

Johannes Späth, Max Helm, Benedikt Jaeger and Georg Carle, "Sim2HW: Modeling Latency Offset Between Network Simulations and Hardware Measurements," Proceedings of 20th International Conference on Emerging Networking Experiments and Technologies (CoNEXT 2024), 3rd Workshop on Graph Neural Networking Workshop (GNNet 2024), Los Angeles, CA, September 2024, pp. 20–26.

Abstract

Network modeling often relies on simulation tools due to their flexibility and cost-effectiveness. However, in many cases, those tools can only cover some aspects of real-world networks accurately. Measurements on hardware testbeds are more accurate but require more resources and configuration and are thus frequently impractical for real-world networks. Graph Neural Networks (GNNs) are a promising machine learning approach proven to be especially useful for learning the properties of computer networks. In this paper, we present a GNN-based approach that uses simulation data as an additional input to predict latency values measured on real hardware. We train our model with an existing dataset from a hardware testbed and show that it can predict the latency distribution in unseen topologies with a MAPE of 27.2 % and an MdAPE of 19.8 %.

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Johannes Späth
Max Helm
Benedikt Jaeger
Georg Carle

BibTeX reference

@inproceedings{spath2024sim2hw,
    author = {Sp{\"{a}}th, Johannes and Helm, Max and Jaeger, Benedikt and Carle, Georg},
    doi = {10.1145/3694811.3697820},
    title = {{Sim2HW: Modeling Latency Offset Between Network Simulations and Hardware Measurements}},
    pages = {20--26},
    publisher = {ACM},
    address = {Los Angeles, CA},
    booktitle = {20th International Conference on Emerging Networking Experiments and Technologies (CoNEXT 2024), 3rd Workshop on Graph Neural Networking Workshop (GNNet 2024)},
    month = {9},
    year = {2024},
   }
   
   

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Last modified: 2026-04-30