Skip to main content

An orchestrator for distributed IP tracing

Project description

🌬️ Zeph

Tests Coverage PyPI

Zeph is a reinforcement learning based algorithm for selecting prefixes to probe based on previous measurements in order to maximize the number of nodes and links discovered. Zeph can be used on top of the Iris platform.

🚀 Quickstart

Zeph has a command line interface to configure and run the algorithm.

First, install the Zeph package:

pip install dioptra-zeph

Zeph takes as input a list of /24 (IPv4) or /64 (IPv6) prefixes:

# prefixes.txt
8.8.8.0/24
2001:4860:4860::/64

To start a measurement from scratch:

zeph prefixes.txt

To start from a previous measurement:

zeph prefixes.txt UUID

Zeph relies on iris-client and pych-client for communicating with Iris and ClickHouse. See their respective documentation to know how to specify the credentials.

✨ Generate prefix lists from BGP RIBs

You can create an exhaustive list of /24 prefixes from a BGP RIB dump:

pyasn_util_download.py --latest
# Connecting to ftp://archive.routeviews.org
# Finding most recent archive in /bgpdata/2022.05/RIBS ...
# Downloading ftp://archive.routeviews.org//bgpdata/2022.05/RIBS/rib.20220524.1000.bz2
#  100%, 659KB/s
# Download complete.
zeph-bgp-convert --print-progress rib.20220524.1000.bz2 prefixes.txt

📚 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}
}

🧑‍💻 Authors

Iris is developed and maintained by the Dioptra group at Sorbonne Université in Paris, France.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dioptra_zeph_elena-1.3.2.tar.gz (11.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

dioptra_zeph_elena-1.3.2-py3-none-any.whl (15.4 kB view details)

Uploaded Python 3

File details

Details for the file dioptra_zeph_elena-1.3.2.tar.gz.

File metadata

  • Download URL: dioptra_zeph_elena-1.3.2.tar.gz
  • Upload date:
  • Size: 11.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.7

File hashes

Hashes for dioptra_zeph_elena-1.3.2.tar.gz
Algorithm Hash digest
SHA256 b55b595c1dd45623b8a8df30e013ff8834da27ad0c2473c7703f59f8e9937d23
MD5 c529e2fd7a4be2f38642291ef2c9735e
BLAKE2b-256 fe6f0beede687ab3ed8658d9e11096237256968dabba9fcfe22768a91c6204c8

See more details on using hashes here.

File details

Details for the file dioptra_zeph_elena-1.3.2-py3-none-any.whl.

File metadata

File hashes

Hashes for dioptra_zeph_elena-1.3.2-py3-none-any.whl
Algorithm Hash digest
SHA256 9836113d1210f2290d122466811d3f684bc7afa592fa7c5739fd083b157588eb
MD5 87f64d66c80521ee14da27629907660e
BLAKE2b-256 f58b8c3afd925e5793b62d32bb94e0ab3d4806d8f92577089e53ec6260d183c9

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page