Making Byzantine Consensus Live

Bravo, Manuel; Chockler, Gregory; Gotsman, Alexey

doi:10.4230/lipics.disc.2020.23

Cited by 7 publications

(13 citation statements)

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“…We show that Byz-GentleRain satisfies Byz-CC. We assume that single-shot PBFT is correct and refer its detailed correctness proof to [5]. Table A1 gives the definitions of the predicates ValidNewLeader and safe propose used in Algorithm 4, which are also adapted from [5].…”

Section: Discussionmentioning

confidence: 99%

“…We assume that each client or server is equipped with a physical clock, which is monotonically increasing. Clocks at different clients are loosely synchronized by a protocol such as NTP 5 . The correctness of Byz-GentleRain does not depend on the precision of clock synchronization, but large clock drifts may negatively impact its performance.…”

Section: System Modelmentioning

confidence: 99%

“…We denote by valid(m) that m is valid in signatures. We also use a cryptographic hash function hash(), which is assumed to be collision-resistant: the probability of an adversary producing inputs m and m such that hash(m) = hash(m ) is negligible [5,8].…”

Section: Failure Modelmentioning

confidence: 99%

“…replicas(p) (Algorithm 4). We follow the pseudocode of single-shot PBFT described in [5], and refer its detailed description and correctness proof to [5]. In the following, we elaborate the parts specific to synchronization of global stable time; see the pseudocode underlined in Algorithm 4.…”

Section: Synchronization Of Global Stable Timementioning

confidence: 99%

“…Then it triggers a newview action with a view larger than curr view p d (line 4:4). As in classic PBFT [5,8], the newview(view ) action can also be triggered spontaneously, due to timeout, or by failure detectors. When it is triggered at a replica r p d , the replica will send a signed newleader message to the leader leader(view ) of view in replicas(p) (line 4:10).…”

Section: Synchronization Of Global Stable Timementioning

confidence: 99%

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Byz-GentleRain: An Efficient Byzantine-tolerant Causal Consistency Protocol

Huang¹,

Wei²,

Huang³

et al. 2021

Preprint

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Causal consistency is a widely used weak consistency model that allows high availability despite network partitions. There are plenty of research prototypes and industrial deployments of causally consistent distributed systems. However, as far as we know, none of them consider Byzantine faults, except Byz-RCM proposed by Tseng et al. Byz-RCM achieves causal consistency in the client-server model with 3f + 1 servers where up to f servers may suffer Byzantine faults, but assumes that clients are non-Byzantine. In this work, we present Byz-Gentlerain, the first causal consistency protocol which tolerates up to f Byzantine servers among 3f + 1 servers in each partition and any number of Byzantine clients. Byz-GentleRain is inspired by the stabilization mechanism of GentleRain for causal consistency. To prevent causal violations due to Byzantine faults, Byz-GentleRain relies on PBFT to reach agreement on a sequence of global stable times and updates among servers, and only updates with timestamps less than or equal to such common global stable times are visible to clients. We prove that Byz-GentleRain achieves Byz-CC, the causal consistency variant in the presence of Byzantine faults. We evaluate Byz-GentleRain on Aliyun. The preliminary results show that Byz-GentleRain is efficient on typical workloads.

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Section: Discussionmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: Failure Modelmentioning

confidence: 99%

Section: Synchronization Of Global Stable Timementioning

confidence: 99%

Section: Synchronization Of Global Stable Timementioning

confidence: 99%

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Byz-GentleRain: An Efficient Byzantine-tolerant Causal Consistency Protocol

Huang¹,

Wei²,

Huang³

et al. 2021

Preprint

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Quorum Tree Abstractions of Consensus Protocols

Çirişci

Enea

Mutluergil

2023

Lecture Notes in Computer Science

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Distributed algorithms solving agreement problems like consensus or state machine replication are essential components of modern fault-tolerant distributed services. They are also notoriously hard to understand and reason about. Their complexity stems from the different assumptions on the environment they operate with, i.e., process or network link failures, Byzantine failures etc. In this paper, we propose a novel abstract representation of the dynamics of such protocols which focuses on quorums of responses (votes) to a request (proposal) that form during a run of the protocol. We show that focusing on such quorums, a run of a protocol can be viewed as working over a tree structure where different branches represent different possible outcomes of the protocol, the goal being to stabilize on the choice of a fixed branch. This abstraction resembles the description of recent protocols used in Blockchain infrastructures, e.g., the protocol supporting Bitcoin or Hotstuff. We show that this abstraction supports reasoning about the safety of various algorithms, e.g., Paxos, PBFT, Raft, and HotStuff, in a uniform way. In general, it provides a novel induction based argument for proving that such protocols are safe.

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