Trees and Turtles: Modular Abstractions for State Machine Replication Protocols
April 16, 2023 Β· Declared Dead Β· π PaPoC@EuroSys
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Natalie Neamtu, Haobin Ni, Robbert van Renesse
arXiv ID
2304.07850
Category
cs.DC: Distributed Computing
Citations
0
Venue
PaPoC@EuroSys
Last Checked
3 months ago
Abstract
We present two abstractions for designing modular state machine replication (SMR) protocols: trees and turtles. A tree captures the set of possible state machine histories, while a turtle represents a subprotocol that tries to find agreement in this tree. We showcase the applicability of these abstractions by constructing crash-tolerant SMR protocols out of abstract tree turtles and providing examples of tree turtle implementations. Tree turtles can also be extended to be made Byzantine fault-tolerant (BFT). The modularity of tree turtles allows a generic approach for adding a leader for liveness. We expect that these abstractions will simplify reasoning and formal verification of SMR protocols as well as facilitate innovation in protocol designs.
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