This post is about the classic result from 1983 on authenticated broadcast against a Byzantine adversary:
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The FLP Impossibility, Asynchronous Consensus Lower Bound via Uncommitted Configurations
In this third post, we conclude with the celebrated Fischer, Lynch, and Paterson impossibility result from 1985. It is the fundamental lower bound for consensus in the asynchronous model.
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Synchronous Consensus Lower Bound via Uncommitted Configurations
In this second post, we show the fundamental lower bound on the number of rounds for consensus protocols in the synchronous model.
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Consensus Lower Bounds via Uncommitted Configurations
In this series of three posts, we discuss two of the most important consensus lower bounds: Lamport, Fischer [1982]: any protocol solving consensus in the synchronous model that is resilient to $t$ crash failures must have an execution with at least $t+1$ rounds. Fischer, Lynch, and Patterson [1983, 1985]: any protocol solving consensus in the asynchronous model that is resilient to even one crash failure must have an infinite execution....
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Data, Consensus, Execution: Three Scalability Bottlenecks for State Machine Replication
If anyone asks you: how can I scale my State Machine Replication (Blockchain) system?
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Security proof for Nakamoto Consensus
Bitcoin’s underlying consensus protocol, now known as Nakamoto consensus, is an extremely simple and elegant solution to the Byzantine consensus problem. One may expect this simple protocol to come with a simple security proof. But that turns out not to be the case. The Bitcoin white paper did not provide a proof. Several academic papers (e.g. Garay, Kiayias, Leonardos, and Pass, Seeman, shelat and Kiffer, Rajaraman, shelat) later presented rigorous...
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Sync HotStuff, A Simple and Practical State Machine Replication
In the previous post, we discussed progress in authenticated synchronous consensus protocols. In this post, we will discuss one of the recent protocols Sync HotStuff, which is a simple and practical Byzantine Fault Tolerant SMR protocol to tolerate $f < n/2$ faults.
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Authenticated Synchronous BFT
Post updated in March 2021
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Primary-Backup for Two Servers and One Omission Failure is Impossible
In the previous post, we show that State Machine Replication for any f<n failures is possible in the synchronous model when the adversary can only cause parties to crash. In this post, we show that omission failures are more challenging. It requires f<n/2.
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Primary-Backup State Machine Replication for Crash Failures
We continue our series of posts on State Machine Replication (SMR). In this post we discuss the most simple form of SMR: Primary-Backup for crash failures. We will assume synchronous communication. For simplicity, we will consider the case with two replicas, out of which one can crash. Recall that when a party crashes, it irrevocably terminates.
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A Payment Channel is a two person BFS-SMR system
This posts views payment channels as essentially a two person BFS-SMR system along with a carefully implemented mechanism for safe termination (channel closing) under assumptions of synchrony.
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Flavours of State Machine Replication
State Machine Replication is a fundamental approach in distributed computing for building fault tolerant systems. This post is a followup to our basic post on Fault Tolerant State Machine Replication.
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Flavours of Broadcast
What is the difference between broadcast, crusader broadcast, gradecast, weak broadcast, detectable broadcast, and broadcast with abort? This post is a follow up to our basic post on: What is Broadcast?
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Consensus for State Machine Replication
We introduced definitions for consensus, Byzantine Broadcast (BB) and Byzantine Agreement (BA), in an earlier post. In this post, we will discuss how consensus protocols are used in State Machine Replication (SMR). We will compare and contrast this setting to that of traditional BB and BA.
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Flavours of Partial Synchrony
This is a follow up post to the post on Synchrony, Asynchrony and Partial synchrony. The partial synchrony model of DLS88 comes in two flavours: GST and Unknown Latency. In this post we discuss:
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