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https://github.com/codex-storage/bittorrent-benchmarks.git
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87 lines
2.7 KiB
Plaintext
87 lines
2.7 KiB
Plaintext
---
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title: "Analysis for Deluge Benchmarks - Static Network Dissemination Experiment"
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output:
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bookdown::html_notebook2:
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number_sections: TRUE
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toc: TRUE
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date: "2025-01-15"
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---
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This document contains the analysis for the Deluge benchmarks.
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```{r message=FALSE}
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library(tidyverse)
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devtools::load_all()
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```
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# Parse/Load Data
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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).
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```{r}
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deluge <- read_all_experiments('./data/deluge')
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```
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Computes the benchmark statistics from raw download logs.
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```{r}
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benchmarks <- lapply(deluge, function(experiment) {
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print(glue::glue('Process {experiment$experiment_id}'))
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download_time_stats <- tryCatch({
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meta <- experiment$meta
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completion <- experiment |>
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download_times() |>
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completion_time_stats()
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if (is.null(completion)) {
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NULL
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} else {
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completion |> mutate(
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network_size = meta$nodes$network_size,
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seeders = meta$seeders,
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leechers = network_size - meta$seeders,
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file_size = meta$file_size
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)
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}
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}, error = function(e) { print(e); NULL })
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}) |>
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drop_nulls() |>
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bind_rows() |>
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arrange(file_size, network_size, seeders, leechers) |>
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mutate(
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file_size = as.character(rlang::parse_bytes(as.character(file_size))),
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seeder_ratio = seeders/network_size
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) |>
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relocate(file_size, network_size, seeders, leechers)
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```
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# Results
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First, we present the raw data in tabular format:
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```{r}
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benchmarks
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```
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We then plot the median by network size, and facet it by seeder ratio and file size to see if looks sane:
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```{r fig.width = 10, warning=FALSE, message=FALSE}
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ggplot(benchmarks) +
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geom_line(aes(x = network_size, y = median)) +
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geom_point(aes(x = network_size, y = median)) +
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ylab('median download time (seconds)') +
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xlab('network size') +
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theme_minimal(base_size=15) +
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facet_grid(
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file_size ~ seeder_ratio,
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scales = 'free_y',
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labeller = labeller(
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file_size = as_labeller(function(x) x),
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seeder_ratio = as_labeller(function(x) {
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paste0("seeder ratio: ", scales::percent(as.numeric(x)))
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}))
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) +
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ylim(c(0,NA))
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```
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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.
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