codex-storage/bittorrent-benchmarks
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feat: complete the final analysis

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gmega 2025-01-15 10:22:18 -03:00
parent 1ee8ea8a35
commit 47646eb02a
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2 changed files with 34 additions and 10 deletions
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analysis/final.analysis/R/analysis.R
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@ -37,7 +37,7 @@ check_incomplete_downloads <- function(deluge_torrent_download, n_pieces) {
} }
check_mismatching_repetitions <- function(deluge_torrent_download, repetitions) { check_mismatching_repetitions <- function(deluge_torrent_download, repetitions) {
mismatching_repetitions <- downloads |> mismatching_repetitions <- deluge_torrent_download |>
select(seed_set, node, run) |> select(seed_set, node, run) |>
distinct() |> distinct() |>
group_by(seed_set, node) |> group_by(seed_set, node) |>

42
analysis/final.analysis/static-dissemination.Rmd
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@ -1,18 +1,28 @@
--- ---
title: "static-dissemination.Rmd" title: "Analysis for Deluge Benchmarks - Static Network Dissemination Experiment"
output: html_document output:
date: "2025-01-10" bookdown::html_notebook2:
number_sections: TRUE
toc: TRUE
date: "2025-01-15"
--- ---
```{r} This document contains the analysis for the Deluge benchmarks.
```{r message=FALSE}
library(tidyverse) library(tidyverse)
devtools::load_all() devtools::load_all()
``` ```
# Parse/Load Data
This is data that's been pre-parsed from an experiment [log source](https://github.com/codex-storage/bittorrent-benchmarks/blob/1ee8ea8a35a2c0fccea6e7c955183c4ed03eebb3/benchmarks/logging/sources.py#L27).
```{r} ```{r}
deluge <- read_all_experiments('./data/deluge') deluge <- read_all_experiments('./data/deluge')
``` ```
Computes the benchmark statistics from raw download logs.
```{r} ```{r}
benchmarks <- lapply(deluge, function(experiment) { benchmarks <- lapply(deluge, function(experiment) {
@ -45,18 +55,32 @@ benchmarks <- lapply(deluge, function(experiment) {
relocate(file_size, network_size, seeders, leechers) relocate(file_size, network_size, seeders, leechers)
``` ```
# Results
First, we present the raw data in tabular format:
```{r} ```{r}
benchmarks benchmarks
``` ```
We then plot the median by network size, and facet it by seeder ratio and file size to see if looks sane:
```{r} ```{r fig.width = 10, warning=FALSE, message=FALSE}
ggplot(benchmarks |> filter(file_size == '104.86 MB')) + ggplot(benchmarks) +
geom_line(aes(x = network_size, y = median)) + geom_line(aes(x = network_size, y = median)) +
geom_point(aes(x = network_size, y = median)) + geom_point(aes(x = network_size, y = median)) +
ylab('median download time (seconds)') + ylab('median download time (seconds)') +
xlab('network size') + xlab('network size') +
theme_minimal() + theme_minimal(base_size=15) +
facet_wrap(seeder_ratio ~ file_size) facet_grid(
file_size ~ seeder_ratio,
scales = 'free_y',
labeller = labeller(
file_size = as_labeller(function(x) x),
seeder_ratio = as_labeller(function(x) {
paste0("seeder ratio: ", scales::percent(as.numeric(x)))
}))
) +
ylim(c(0,NA))
``` ```
The data looks largely sane: a larger seeder ratio makes performance somewhat better; though not nearly as consistently as one would hope, at least in this data, and there is a linear performance degradation trend as the network grows larger. Also, the $100\text{MB}$ file seems to generate much better-behaved data than the $1\text{GB}$ case with those trends; i.e., larger seeder ratio improving performance, and network size linearly degrading it, being more pronounced.