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RiskNet: Neural Risk Assessment in Networks of Unreliable Resources

Author

Listed:
  • Krzysztof Rusek
  • Piotr Bory{l}o
  • Piotr Jaglarz
  • Fabien Geyer
  • Albert Cabellos
  • Piotr Cho{l}da

Abstract

We propose a graph neural network (GNN)-based method to predict the distribution of penalties induced by outages in communication networks, where connections are protected by resources shared between working and backup paths. The GNN-based algorithm is trained only with random graphs generated with the Barab\'asi-Albert model. Even though, the obtained test results show that we can precisely model the penalties in a wide range of various existing topologies. GNNs eliminate the need to simulate complex outage scenarios for the network topologies under study. In practice, the whole design operation is limited by 4ms on modern hardware. This way, we can gain as much as over 12,000 times in the speed improvement.

Suggested Citation

  • Krzysztof Rusek & Piotr Bory{l}o & Piotr Jaglarz & Fabien Geyer & Albert Cabellos & Piotr Cho{l}da, 2022. "RiskNet: Neural Risk Assessment in Networks of Unreliable Resources," Papers 2201.12263, arXiv.org, revised Jun 2023.
    • Handle: RePEc:arx:papers:2201.12263
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    References listed on IDEAS

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    1. Righi, Marcelo Brutti & Ceretta, Paulo Sergio, 2015. "A comparison of Expected Shortfall estimation models," Journal of Economics and Business, Elsevier, vol. 78(C), pages 14-47.
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