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Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/20.500.12008/49702 Cómo citar
Título: Building reliability-improving network transformations.
Autor: Canale, Eduardo
Robledo, Franco
Romero, Pablo
Viera, Julián
Tipo: Preprint
Palabras clave: Computer network reliability, Reliability engineering, Peer-to-peer computing, Surgery, Reliability theory, Communication networks, Network Reliability Analysis, Uniformly Most-Reliable Graphs, Swing Surgery, Yutsis Graph
Fecha de publicación: 2019
Resumen: A fundamental problem in network reliability analysis is to find the connectedness probability of a random graph subject to perfect nodes and independent link failures with identical probabilities. This connectedness probability is called the all-terminal reliability, and its determination is a challenging problem. In this paper we study the corresponding network design problem: which is the best way to connect q links among p nodes in order to maximize the all-terminal reliability? The optimal solutions to this problem are called uniformly most-reliable graphs. To the best of our knowledge, the literature offers a single reliability-improving network transformation called swing surgery, credited to Kelmans. Here, we offer two novel reliability-improving transformations which, even simple, they help to improve the reliability in a uniform sense (i.e., under all feasible link failure probability). Supported by the previous transformations, we prove that Yutsis graph is uniformly most-reliable, which is currently a computationally prohibitive problem. Conjectures and open problems are also discussed.
Citación: Canale, E., Robledo, F., Romero, P. y otros. Building reliability-improving network transformations [Preprint]. Publicado en: 2019 15th International Conference on the Design of Reliable Communication Networks (DRCN), Coimbra, Portugal, 19-21 mar 2019, pp 107-113. DOI: 10.1109/DRCN.2019.8713759.
Aparece en las colecciones: Publicaciones académicas y científicas - IMERL (Instituto de Matemática y Estadística Rafael Laguardia)

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