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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Dont Trust. Verify. and Checkpoint?
Imagine that that Aliens land on earth with a new superfast SHA256 machine. Imagine this machine always gives them more than 51% of the current world Bitcoin hash power (but not enough hash power to completely break SHA256). Suppose they decide to build a chain from the Bitcoin Genesis block that is longer than any other chain on earth and put only empty blocks on it. Could they erase all...
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Does Byzantine Agreement need Quadratic Messages?
The quest for building scalable Byzantine agreement has many challenges. In this post we highlight the 1982 Dolev and Reischuk lower bound.
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Byzantine Agreement is Impossible for $n \leq 3 f$ if the Adversary can Simulate
The Fischer, Lynch, and Merritt, 1985 lower bound states that Byzantine agreement is impossible if the adversary controls $f>n/3$ parties. It is well known that this lower bound does not hold if there is a PKI setup.
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The Trusted Setup Phase
co-authored with Avishay Yanai
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Do Bitcoin and Ethereum have any trusted setup assumptions?
co-authored with Alin Tomescu and Avishay Yanai
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What is Consensus?
We all broadly understand “consensus” as the notion of different parties agreeing with each other. In distributed computing, Consensus is one of the core functionalities. In this post, we define the consensus problem and discuss some variants and their differences.
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Byzantine Agreement is impossible for $n \leq 3 f$ under partial synchrony
Lower bounds in distributed computing are very helpful. Obviously, they prevent you from wasting time trying to do impossible things :-). Even more importantly, understanding them well often helps in finding ways to focus on what is optimally possible or ways to circumvent them by altering the assumptions or problem formulation.
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What is the difference between PBFT, Tendermint, SBFT and HotStuff ?
In this post I will try to compare four of my favorite protocols for Byzantine Fault Tolerant (BFT) State Machine Replication (SMR):
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The threshold adversary
Once we fix the communication model (synchrony, asynchrony, or partial synchrony see here), we then need some way to limit the power of the adversary.
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The power of the adversary
Once we fix the communication model (synchrony, asynchrony, or partial synchrony see here), and we fix a threshold adversary we still need to make important modeling decisions about the adversary power.
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Synchrony, Asynchrony and Partial synchrony
In the standard distributed computing model, the communication uncertainty is captured by an adversary that can control the message delays. The communication model defines the limits to the power of the adversary to delay messages.
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