RiskNet: Neural Risk Assessment in Networks of Unreliable Resources

Autor:	Rusek, Krzysztof, Boryło, Piotr, Jaglarz, Piotr, Geyer, Fabien, Cabellos, Albert, Chołda, Piotr
Rok vydání:	2022
Předmět:	Computer Science - Networking and Internet Architecture Computer Science - Machine Learning Quantitative Finance - Risk Management Statistics - Machine Learning I.2 C.2
Druh dokumentu:	Working Paper
Popis:	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. Comment: This paper is under consideration at Journal of Network and Systems Management
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2201.12263 Zobrazit plný text záznamu View this record from Arxiv