拜占庭网络中的分区检测

Yérom-David BrombergIRISA, UR, Jérémie DecouchantTU Delft, Manon SourisseauIRISA, UR, François TaïaniIRISA, UR
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摘要

检测和处理网络分区是分布式系统的基本要求。尽管现有的任意图分区检测方法可以容忍不可靠的网络,但它们要么假定所有节点都是正确的,要么假定有限数量的节点可能会崩溃。尤其是拜占庭行为,尽管拜占庭容错是共识等重要问题的一个活跃研究课题,但拜占庭容错却不在这些算法的研究范围内。此外,拜占庭容错协议(如广播或共识)总是依赖于连接网络的假设。本文探讨了拜占庭网络(无连接性假设)中的分区检测问题。我们提出了一种新颖的算法,称为 NECTAR,它能安全地检测分区和可能可分区的网络,并证明其正确性。NECTAR 允许所有正确节点检测网络是否可能存在拜占庭节点。我们评估了 NECTAR 的性能,并使用多达 100 个运行真实代码的节点在各种现实拓扑结构上将其与两个现有基线进行了比较。我们的结果证实,NECTAR 保持了 100% 的准确率,而只要有一个参与者是拜占庭成员,现有各种基线的准确率就会下降至少 40%。虽然 NECTAR 的网络成本随节点数增加而增加,随网络直径减小而减小,但在最糟糕的情况下也不会超过 500KB 左右。
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Partition Detection in Byzantine Networks
Detecting and handling network partitions is a fundamental requirement of distributed systems. Although existing partition detection methods in arbitrary graphs tolerate unreliable networks, they either assume that all nodes are correct or that a limited number of nodes might crash. In particular, Byzantine behaviors are out of the scope of these algorithms despite Byzantine fault tolerance being an active research topic for important problems such as consensus. Moreover, Byzantinetolerant protocols, such as broadcast or consensus, always rely on the assumption of connected networks. This paper addresses the problem of detecting partition in Byzantine networks (without connectivity assumption). We present a novel algorithm, which we call NECTAR, that safely detects partitioned and possibly partitionable networks and prove its correctness. NECTAR allows all correct nodes to detect whether a network could suffer from Byzantine nodes. We evaluate NECTAR's performance and compare it to two existing baselines using up to 100 nodes running real code, on various realistic topologies. Our results confirm that NECTAR maintains a 100% accuracy while the accuracy of the various existing baselines decreases by at least 40% as soon as one participant is Byzantine. Although NECTAR's network cost increases with the number of nodes and decreases with the network's diameter, it does not go above around 500KB in the worst cases.
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