@inproceedings{a2318c5964cd4dd3b79f1877fc35480e,
title = "Sim2HW: Modeling Latency Offset Between Network Simulations and Hardware Measurements",
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 %.",
keywords = "Graph Neural Network, Hardware Measurement, Latency Model, Network Simulation",
author = "Johannes Sp{\"a}th and Max Helm and Benedikt Jaeger and Georg Carle",
note = "Publisher Copyright: {\textcopyright} 2024 Copyright held by the owner/author(s).; 3rd International Workshop on Graph Neural Networking, GNNet 2024, co-located with ACM CoNEXT 2024 ; Conference date: 09-12-2024 Through 12-12-2024",
year = "2024",
month = dec,
day = "9",
doi = "10.1145/3694811.3697820",
language = "English",
series = "GNNet 2024 - Proceedings of the 3rd GNNet Workshop on Graph Neural Networking Workshop, Co-Located with: CoNEXT 2024",
publisher = "Association for Computing Machinery, Inc",
pages = "20--26",
booktitle = "GNNet 2024 - Proceedings of the 3rd GNNet Workshop on Graph Neural Networking Workshop, Co-Located with",
}