Fast-HotStuff: A Fast and Resilient HotStuff Protocol
October 22, 2020 Β· Declared Dead Β· π IEEE Transactions on Dependable and Secure Computing
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mohammad M. Jalalzai, Jianyu Niu, Chen Feng, Fangyu Gai
arXiv ID
2010.11454
Category
cs.DC: Distributed Computing
Citations
44
Venue
IEEE Transactions on Dependable and Secure Computing
Last Checked
6 months ago
Abstract
The HotStuff protocol is a breakthrough in Byzantine Fault Tolerant (BFT) consensus that enjoys both responsiveness and linear view change. It creatively adds an additional round to classic BFT protocols (like PBFT) using two rounds. This brings us to an interesting question: Is this additional round really necessary in practice? In this paper, we answer this question by designing a new two-round BFT protocol called Fast-HotStuff, which enjoys responsiveness and efficient view change that is comparable to linear view change in terms of performance. Compared to (three-round) HotStuff, Fast-HotStuff has lower latency and is more robust against performance attacks that HotStuff is susceptible to.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Distributed Computing
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Reproducing GW150914: the first observation of gravitational waves from a binary black hole merger
R.I.P.
π»
Ghosted
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
R.I.P.
π»
Ghosted
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
R.I.P.
π»
Ghosted
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
R.I.P.
π»
Ghosted
iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted