Elevator: Self-* and Persistent Hub Sampling Service in Unstructured Peer-to-Peer Networks - Sorbonne Université
Reports (Technical Report) Year : 2024

Elevator: Self-* and Persistent Hub Sampling Service in Unstructured Peer-to-Peer Networks

Abstract

We present Elevator, a novel algorithm for hub sampling in peer-to-peer networks, enabling the construction of overlays with a topology between a random graph and a star network, and networks that have both hubs and are resilient to failures. Our approach emerges from principles of preferential attachment, forming hubs spontaneously, offering an innovative solution for decentralized networks that can benefit use cases requiring a network with both low diameter and resilience to failures.
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Dates and versions

hal-04582174 , version 1 (21-05-2024)
hal-04582174 , version 2 (11-06-2024)
hal-04582174 , version 3 (29-08-2024)
hal-04582174 , version 4 (14-10-2024)

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Mohamed Amine Legheraba, Maria Potop-Butucaru, Sébastien Tixeuil. Elevator: Self-* and Persistent Hub Sampling Service in Unstructured Peer-to-Peer Networks. Sorbonne Universites, UPMC University of Paris 6; LIP6 - Laboratoire d'Informatique de Paris 6. 2024. ⟨hal-04582174v2⟩
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