Szczegóły publikacji
Opis bibliograficzny
RiskNet: neural risk assessment in networks of unreliable resources / Krzysztof RUSEK, Piotr BORYŁO, Piotr JAGLARZ, Fabien Geyer, Albert Cabellos, Piotr CHOŁDA // Journal of Network and Systems Management ; ISSN 1064-7570. — 2023 — vol. 31 iss. 3 art. no. 64, s. 1–22. — Bibliogr. s. 19–20, Abstr. — Publikacja dostępna online od: 2023-07-15
Autorzy (6)
- AGHRusek Krzysztof
- AGHBoryło Piotr
- AGHJaglarz Piotr
- Geyer Fabien
- Cabellos-Aparicio Albert
- AGHChołda Piotr
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 147863 |
|---|---|
| Data dodania do BaDAP | 2023-09-08 |
| Tekst źródłowy | URL |
| DOI | 10.1007/s10922-023-09755-y |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Journal of Network and Systems Management |
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 on the basis of the Barabási–Albert model. However, the results obtained show that we can accurately model the penalties in a wide range of existing topologies. We show that GNNs eliminate the need to simulate complex outage scenarios for the network topologies under study—in practice, the entire time of path placement evaluation based on the prediction is no longer than 4 ms on modern hardware. In this way, we gain up to 12 000 times in speed improvement compared to calculations based on simulations.