Important Notice

DUE TO SUPPORT ISSUES, IRIS IS NOT CURRENTLY AVAILABLE AS A SERVICE TO THE PUBLIC TO RUN THEIR MEASUREMENTS. WE HOPE TO MAKE IRIS AVAILABLE AGAIN TO THE PUBLIC IN THE NEAR FUTURE.

🌬️ Zeph — Evaluation

This repository contains the code used to produce the results of the evalution section of the Zeph & Iris paper.

The Python notebooks provided in this repository allow you to:

• perform your own measurements from the Iris platform to reproduce the dataset used in the paper
• reproduce the analysis presented in the paper, either on your own dataset, or on the original dataset used in the paper

In addition, the source code of Zeph and Iris are available in the dioptra-io/zeph and dioptra-io/iris repositories.

🧪 Experiments

Prerequisites

1. Copy the sample configuration file config.example.json to config.json and fill-in your Iris credentials.
2. Download zeph-evaluation-dataset.tar.gz (650MB) and extract it at the root of the repository:

curl -L https://minio.iris.dioptra.io/public/zeph-evaluation-dataset.tar.gz | tar x

Notebooks

Two notebooks are provided for each section: the execution notebook which contains the code to perform the measurements, and the analysis notebook which contains the code to analyse the measurement results and generate the plots.

Section	Execution	Analysis
§6.2.1 — Zeph's reinforcement learning approach outperforms random allocation	zeph_allocation_execution.ipynb	zeph_allocation_analysis.ipynb
§6.2.2 — Zeph/Iris conducting multipath traceroutes performs competitively with respect to current state-of-the-art internet scale topology discovery systems	zeph_topology_execution.ipynb	zeph_topology_analysis.ipynb
§6.3 — Zeph probe savings	zeph_savings_execution.ipynb	zeph_savings_analysis.ipynb
§6.4 — Reinforcement learning analysis	zeph_savings_execution.ipynb	rl_analysis.ipynb

📚 Publications

📚 Publications

@article{10.1145/3523230.3523232,
author = {Gouel, Matthieu and Vermeulen, Kevin and Mouchet, Maxime and Rohrer, Justin P. and Fourmaux, Olivier and Friedman, Timur},
title = {Zeph & Iris Map the Internet: A Resilient Reinforcement Learning Approach to Distributed IP Route Tracing},
year = {2022},
issue_date = {January 2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {52},
number = {1},
issn = {0146-4833},
url = {https://doi.org/10.1145/3523230.3523232},
doi = {10.1145/3523230.3523232},
journal = {SIGCOMM Comput. Commun. Rev.},
month = {mar},
pages = {2–9},
numpages = {8},
keywords = {active internet measurements, internet topology}
}