input-output-hk/ouroboros-leios
Feb 2025 – Feb 2025
1 Month active
Languages Used
PythonRust
Technical Skills
Data ModelingNetwork AnalysisOptimization AlgorithmsProbabilistic ModelingPython ScriptingRust Programming
Roland Kuhn
PROFILE
Roland Kuhn
Developed and delivered Delta-Q Diffusion Analysis for the input-output-hk/ouroboros-leios topology checker, replacing traditional hop-count diffusion with a probabilistic model to enhance the accuracy of network information flow assessments. Refactored latency calculations to support ΔQ modeling and introduced new ΔQSD analysis modules, enabling more precise and data-driven topology evaluations. Integrated optimization algorithms to fit probabilistic models to real topology data, supporting improved network resilience and performance. The work leveraged Python scripting and Rust programming, combining data modeling, network analysis, and probabilistic modeling to provide a more robust foundation for topology checking and decision support within distributed systems.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,095
Activity Months1
Work History
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered Delta-Q Diffusion Analysis for the Ouroboros-Leios topology checker, introducing a probabilistic diffusion model to replace simple hop-count representations. Refactored latency calculations to support ΔQ modeling, added new ΔQSD analysis modules, and integrated optimization libraries to fit models to topology data. This work enhances accuracy of information diffusion modeling, enabling more reliable topology assessments and data-driven optimization decisions, thereby improving network resilience and performance.
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered Delta-Q Diffusion Analysis for the Ouroboros-Leios topology checker, introducing a probabilistic diffusion model to replace simple hop-count representations. Refactored latency calculations to support ΔQ modeling, added new ΔQSD analysis modules, and integrated optimization libraries to fit models to topology data. This work enhances accuracy of information diffusion modeling, enabling more reliable topology assessments and data-driven optimization decisions, thereby improving network resilience and performance.
February 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonRust
Technical Skills
Data ModelingNetwork AnalysisOptimization AlgorithmsProbabilistic ModelingPython ScriptingRust Programming
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