tailor-aoi
Python implementation of TAILOR, the age-optimal joint sampling and preemption policy from Taming the Heavy Tail: Age-Optimal Preemption (A. Li, Y. İnce, E. Uysal, IEEE ISIT 2026, DOI 10.1109/ISIT62367.2026.11653616).
Install
pip install tailor-aoi # core
pip install "tailor-aoi[plot]" # with matplotlib helpers
Use
from tailor_aoi import ServiceDist, solve, compare
dist = ServiceDist.pareto2(mean=2.0, variance=40.0) # or lognormal_from_mean, exponential, from_scipy
policy = solve(dist, kappa_s=1.0, kappa_p=1.0) # optimal sampling + preemption policy
print(compare(dist, 1.0, 1.0, policy=policy)) # vs. Update-or-Wait, Zero-Wait, best static
Command line: python -m tailor_aoi --dist lognormal --mean 2 --sigma2 6.
- Repository and MATLAB code: https://github.com/AiminLi-Hi/aoi-preemption-heavy-tail
- Colab quickstart: https://colab.research.google.com/github/AiminLi-Hi/aoi-preemption-heavy-tail/blob/main/python/examples/quickstart.ipynb
- Project page: https://yigiti.github.io/Preemption/
Citation
If you use this package, please cite the paper (python -m tailor_aoi --cite prints the BibTeX):
@inproceedings{li2026taming,
title = {Taming the Heavy Tail: Age-Optimal Preemption},
author = {Li, Aimin and {\.I}nce, Yi{\u{g}}it and Uysal, Elif},
booktitle = {Proc. IEEE International Symposium on Information Theory (ISIT)},
year = {2026},
doi = {10.1109/ISIT62367.2026.11653616}
}
1 maintainer
Metadata
1 maintainer
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