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Knowledge Connectivity Requirements for Solving Byzantine Consensus with Unknown Participants

Authors: Eduardo Adílio Pelinson Alchieri; Alysson Bessani; Fabíola Greve; Joni da Silva Fraga;

Knowledge Connectivity Requirements for Solving Byzantine Consensus with Unknown Participants

Abstract

Consensus is a fundamental building block to solve many practical problems that appear on reliable distributed systems. In spite of the fact that consensus is being widely studied in the context of standard networks, few studies have been conducted in order to solve it in dynamic and self-organizing systems characterized by unknown networks. While in a standard network the set of participants is static and known, in an unknown network, such set and number of participants are previously unknown. This work studies the problem of Byzantine Fault-Tolerant Consensus with Unknown Participants , namely BFT-CUP. This new problem aims at solving consensus in unknown networks with the additional requirement that participants in the system may behave maliciously. It presents the necessary and sufficient knowledge connectivity conditions in order to solve BFT-CUP under minimal synchrony requirements. In this way, it proposes algorithms that are shown to be optimal in terms of synchrony and knowledge connectivity among participants in the system.

Keywords

Distributed Agreement, Consensus with Unknown Participants, Byzantine Fault Tolerance, Self-organizing Systems.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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