# ------------------------------------------------------------------ # 3. MONTE‑CARLO ESTIMATE (Algorithm 1) # ------------------------------------------------------------------ def ttl_survival_mc(TTL0, mu, delta=1, max_hops=500, n_rep=100_000): hop_times = np.random.exponential(1/mu, size=(n_rep, max_hops)) cum_times = np.cumsum(hop_times, axis=1)
“I finished it,” she said. “Just not the way they wanted.” maria alejandra ttl model
| Resource | Type | Link / How to Access | |----------|------|----------------------| | – “A Stochastic TTL Model for Heterogeneous Networks” | IEEE/ACM journal article (open‑access) | https://doi.org/10.1109/TNET.2022.3158741 | | Supplementary material – full derivations, proofs, and extra figures | PDF (author‑provided) | https://arxiv.org/abs/2207.09834 (also hosted on arXiv) | | Reference implementation – ttl-model Python package (v0.3) | PyPI + GitHub | pip install ttl-model → https://github.com/maria-alejandra/ttl-model | | Data set – Traces from a campus‑wide IPv6 testbed (used for validation) | CSV (≈ 2 GB) | https://doi.org/10.5281/zenodo.7771234 | | Presentation slides – 2022 IEEE INFOCOM talk (15 min) | PDF | https://www.ieee.org/conferences/info2022/alejandra_slides.pdf | | Cite‑ready BibTeX | Bibliographic entry | @articlegomez2022stochastic, title=A Stochastic TTL Model for Heterogeneous Networks, author=Gómez‑López, María Alejandra and Mendoza, Jorge R., journal=IEEE/ACM Transactions on Networking, year=2022, volume=30, number=4, pages=1792–1805, doi=10.1109/TNET.2022.3158741 | It will: The Thousand-Hour Face
Below is a minimal notebook skeleton you can copy‑paste into a new Jupyter file ( ttl_demo.ipynb ). It will: n_rep=100_000): hop_times = np.random.exponential(1/mu
The Thousand-Hour Face