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Related Experiment Video

Updated: Jun 26, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

A topological characterization of stabilizing consensus.

Ulrich Schmid¹, Stephan Felber¹, Hugo Rincon Galeana^1,2

¹Embedded Computing Systems Group E191-02, TU Wien, Treitlstrasse 1-3, 1040 Vienna, Austria.

Distributed Computing

|June 25, 2026

Summary

This summary is machine-generated.

This study characterizes stabilizing consensus solvability using point-set topology. It introduces semi-continuous functions to explain consensus possibilities and impossibilities in computing systems with faults.

Keywords:

Distributed computing impossibility results point-set topology stabilizing consensus

Related Experiment Videos

Last Updated: Jun 26, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Area of Science:

Distributed computing
Point-set topology
Fault-tolerant systems

Background:

Stabilizing consensus is crucial for fault-tolerant distributed systems.
Existing characterizations often rely on specific computing models and fault assumptions.
Topological methods have been successfully applied to terminating consensus.

Purpose of the Study:

To provide a complete topological characterization of deterministic stabilizing consensus.
To extend topological approaches to handle benign process and communication faults.
To unify existing results on stabilizing consensus solvability.

Main Methods:

Utilizing point-set topology and infinite execution topologies.
Applying semi-open decision sets and semi-continuous decision functions.
Analyzing the mapping properties of decision functions between topological spaces.

Main Results:

Established semi-continuous functions as the key tool for characterizing stabilizing consensus.
Demonstrated that semi-continuous functions can map connected spaces to disconnected ones.
Proved the equivalence of multi-valued stabilizing consensus with weak and strong validity.

Conclusions:

The study offers a unified topological framework for understanding stabilizing consensus.
The findings provide a clear topological explanation for known solvability and impossibility results.
This work advances the theoretical foundations of fault-tolerant distributed computing.