Topologies for Blockchain Payment Channel Networks: Models and Constructions

Payment channel networks (PCNs), also known as off-chain networks, implement a common approach to deal with the scalability problem of blockchain networks. They enable users to execute payments without committing them to the blockchain by relying on predefined payment channels. A pair of users can e...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE/ACM transactions on networking 2024-12, Vol.32 (6), p.4781-4797
Hauptverfasser: Khamis, Julia, Kotzer, Arad, Rottenstreich, Ori
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Payment channel networks (PCNs), also known as off-chain networks, implement a common approach to deal with the scalability problem of blockchain networks. They enable users to execute payments without committing them to the blockchain by relying on predefined payment channels. A pair of users can employ a payment even without a direct channel between them, by routing the payment via payment channels involving other intermediate users. Users, together with the channels, form a graph known as the off-chain network topology. The off-chain topology and the payment characteristics affect network performance such as the average number of intermediate users a payment is routed through or the values of transaction fees. In this paper, we study two basic problems in payment channel network design. First, efficiently mapping users to an off-chain topology with a known structure. Second, constructing a topology with a bounded number of channels that can serve users well with associated payments. We design algorithms for both problems while considering several fundamental topologies. We study topology-related real data statistics of Raiden, the off-chain extension for Ethereum as well as of Lightning, the equivalent off-chain layer of Bitcoin. We conduct experiments to demonstrate the effectiveness of the algorithms for these networks.
ISSN:1063-6692
1558-2566
DOI:10.1109/TNET.2024.3445274