mirror of
https://github.com/logos-blockchain/research.git
synced 2026-08-07 11:43:20 +00:00
f51630e509
The study started as a peering-degree question and grew well past it: propagation, adversary exposure, deanonymization and time-to-link, reliability under uniform and correlated churn, messaging redundancy, and cover traffic. The pd name no longer describes it. tools/simulators/blend/pd/ -> tools/simulators/blend/, package src/pd -> src/blend, and reports/blend/pd/ -> reports/blend/. Moved with git mv so history follows. The text substitutions are deliberately narrow. pd is also the conventional pandas alias, and pandas genuinely has a pd.plotting submodule, so a blanket pd. -> blend. rewrite would have corrupted four files. Only package-unambiguous forms were changed: from pd.X, -m pd.X, pd.<our module>, PD_BYTES_BUDGET, src/pd, and the pyproject name. All four import pandas as pd lines are untouched and verified. Both READMEs reframed: peering degree is now presented as the primary axis that ties the others together rather than as the subject, and the relative links, which lost a directory level in the move, are corrected. Verified after the move: ruff clean, 101 tests, 45 verify anchors, make targets, the script shims, an end-to-end smoke run, and data/report_numbers.py still reproducing the report tables from the checked-in evidence. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
30 lines
1.3 KiB
YAML
30 lines
1.3 KiB
YAML
# Correlated (AS/region) churn vs uniform churn, at matched churn fractions.
|
|
#
|
|
# Real outages are not independent: a datacentre, AS or region goes dark as a unit. This config
|
|
# partitions the network into `n_regions` failure domains and compares two ways of removing the
|
|
# SAME number of nodes -- `uniform` (scattered, the §3.5 model) against `regional` (whole domains).
|
|
#
|
|
# Correlation only has a structural effect if the peer graph itself is region-aware: with
|
|
# region-blind peering, taking out whole regions removes a uniformly random set of nodes and is
|
|
# therefore indistinguishable from uniform churn. So `region_locality` places 75% of every node's
|
|
# peers inside its own region, which is what makes a failure domain a *connectivity* domain too.
|
|
# Both churn modes run on the SAME topology, so the comparison is controlled.
|
|
#
|
|
# 800 rounds x 8 seeds = 6400 rounds per cell, matching the churn study in configs/percolation.yaml.
|
|
n_nodes: [20000]
|
|
degree: [4, 8, 16]
|
|
blend_hops: [1, 3]
|
|
max_blend_delay: [0]
|
|
unresponsive_frac: [0.0, 0.2, 0.4, 0.5, 0.6, 0.7, 0.8]
|
|
churn_mode: [uniform, regional]
|
|
redundancy: [1]
|
|
f_adv: [0.2]
|
|
adversary_mode: [random]
|
|
seeds: 8
|
|
base:
|
|
n_regions: 40 # 500 nodes per failure domain
|
|
region_locality: 0.75 # 3 of every 4 peers inside the node's own region
|
|
n_rounds: 800
|
|
n_placements: 1
|
|
worstcase_max_n: 100000
|