mirror of https://github.com/logos-co/scratch.git
committee simulations notebook, gitignore file
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.ipynb_checkpoints
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "b01c279a-cd3f-4e6f-a7ba-fca8d1f42804",
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"metadata": {},
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"outputs": [],
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"source": [
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"import random\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"id": "53cc43b6-051f-43fb-8195-6a753ab74cf1",
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"metadata": {},
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"source": [
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"LogosBFT works by partitioning the nodes into equally-sized committees, then forming a binary tree structure from those committees. A primary (or leader) then partitions the propposed data to be verified via erasure coding techniques (fountain or raptor codes) and distributes it through the tree.\n",
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"\n",
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"This notebook is an attempt to simulate various structures and their respective consequences w.r.t. bandwidth, byzantine behavior, latency, etc. \n",
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"\n",
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"The general process can be described by the following:\n",
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"- Get Random Seed (Blackboxed here)\n",
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"- Form Overlay\n",
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" - Initial Committee Assignment\n",
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" - Overlay Tree Structure Formation\n",
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" - Parent <--> Child Assignment\n",
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"- Leader Selection (Blackboxed here)\n",
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"- Leader Forms Block Proposal (Blackboxed here)\n",
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"- Proposal Erasure Coding\n",
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"- Data Dissemination\n",
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"- Each Committee Forms Block Independently\n",
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"\n",
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"We'd like to track the following metrics:\n",
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"- total bandwidth for an individual node\n",
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"- total num of messages for an individual node\n",
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"- latency of block distribution throughout the network"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2b3761fe-d4dc-438f-8cdb-dbfafeb3b6e5",
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"metadata": {},
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"source": [
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"## Globals definitions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "4da35087-f815-4b88-99f8-04890b839beb",
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"metadata": {},
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"outputs": [],
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"source": [
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"num_nodes = 1000\n",
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"committee_size = 50\n",
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"num_committees = num_nodes / committee_size\n",
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"byzantine_coeff = 0.15\n",
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"max_time_delay = 60 # seconds\n",
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"block_size = 1 # MB"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e1238929-cbe8-4f80-90c0-5823c6a5ddf6",
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"metadata": {},
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"source": [
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"# Form overlay\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "14c20750-375a-410d-aa2b-1b97e17c2ef8",
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"metadata": {},
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"source": [
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"## Initial Committee Assignment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "0bec3377-a302-4bbd-bcfc-3e9c40af51bb",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def chunks(l, n):\n",
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" \"\"\"Yield successive n-sized chunks from l.\"\"\"\n",
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" for i in range(0, len(l), n):\n",
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" yield l[i:i + n]\n",
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"\n",
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"random_node_list = [i for i in range(num_nodes)]\n",
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"random.shuffle(random_node_list)\n",
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"\n",
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"committee_list = list(chunks(random_node_list, committee_size))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"id": "e5917cca-9c2b-423d-b930-6fddfcdea47e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>committee_ind</th>\n",
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" <th>committee_members</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0</td>\n",
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" <td>[506, 929, 199, 437, 427, 686, 861, 597, 81, 5...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>[677, 491, 334, 209, 41, 121, 697, 943, 504, 6...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2</td>\n",
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" <td>[439, 996, 238, 574, 388, 12, 956, 330, 386, 3...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>3</td>\n",
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" <td>[817, 370, 626, 151, 562, 539, 797, 270, 791, ...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>4</td>\n",
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" <td>[288, 693, 282, 348, 777, 671, 545, 222, 477, ...</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" committee_ind committee_members\n",
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"0 0 [506, 929, 199, 437, 427, 686, 861, 597, 81, 5...\n",
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"1 1 [677, 491, 334, 209, 41, 121, 697, 943, 504, 6...\n",
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"2 2 [439, 996, 238, 574, 388, 12, 956, 330, 386, 3...\n",
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"3 3 [817, 370, 626, 151, 562, 539, 797, 270, 791, ...\n",
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"4 4 [288, 693, 282, 348, 777, 671, 545, 222, 477, ..."
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]
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},
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"execution_count": 29,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Create DataFrame of committee assignments and their index\n",
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"committee_df = pd.DataFrame(\n",
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" [{\"committee_ind\": ind, \n",
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" \"committee_members\": committee}\n",
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" for ind, committee in enumerate(committee_list)])\n",
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"committee_df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"id": "903154d0-9cc0-4e96-9c07-6fba378eb063",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"50\n",
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"50\n",
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"50\n",
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"50\n"
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]
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}
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],
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"source": [
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"# Sanity check that all buckets are the same (or close) in size\n",
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"for committee in committee_df[\"committee_members\"].values:\n",
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" print(len(committee))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d3a9986e-7720-43b2-94fb-b4c6b8b43a05",
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"metadata": {},
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"source": [
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"## Form Overlay Tree Structure"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b10a466b-f2d5-4a8f-9572-b2ca2ada520d",
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"metadata": {},
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"source": [
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"## Parent <> Child Assignment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "68de5fdc-0900-4e95-b2d0-57e5c98cb30c",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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