commit 80c885ec4b89986374c699939ff9611cbfebc07e Author: Karsten Loesing karsten.loesing@gmx.net Date: Thu Aug 2 19:04:02 2012 +0200
Graphing code for fast exits graphs (#6498). --- task-6498/.gitignore | 2 + task-6498/plot.R | 77 ++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 79 insertions(+), 0 deletions(-)
diff --git a/task-6498/.gitignore b/task-6498/.gitignore index 9dc71c8..707150a 100644 --- a/task-6498/.gitignore +++ b/task-6498/.gitignore @@ -4,4 +4,6 @@ lib/ bin/ in/ results.csv +Rplots.pdf +*.png
diff --git a/task-6498/plot.R b/task-6498/plot.R new file mode 100644 index 0000000..9f7763d --- /dev/null +++ b/task-6498/plot.R @@ -0,0 +1,77 @@ +# uncomment for 2+ years +#CUTOFF <- "2010-05-26" +#DETAIL <- "week" +CUTOFF <- "2012-06-02" +DETAIL <- "day" + +require(ggplot2) +require(reshape) +require(RColorBrewer) +data <- read.csv("results.csv", stringsAsFactors = FALSE) +data <- data[data$valid_after >= paste(CUTOFF, "00:00:00"), ] + +r <- data +r <- r[r$min_rate == 11875 & r$ports == "80-443-554-1755" & + r$min_advbw == 5000, ] +r <- aggregate(list(relays = r$relays, P_exit = 100 * r$exit_prob), + by = list(date = as.Date(cut.Date(as.Date(r$valid_after), DETAIL))), + FUN = median) +r <- melt(r, id.vars = c("date")) +r <- data.frame(r, type = ifelse(r$variable == "P_exit", + "Total exit probability (in %)", "Number of relays")) +ggplot(r, aes(x = date, y = value)) + +geom_line(colour = "purple", size = 0.75) + +facet_grid(type ~ ., scales = "free_y") + +scale_x_date(name = "") + +scale_y_continuous(name = "") + +scale_colour_manual(values = c("purple", "orange")) + +opts(title = paste("Fast relays (95+ Mbit/s configured bandwidth rate,\n", + "5000+ KB/s advertised bandwidth capacity,\n", + "exit to ports 80, 443, 554, and 1755,\n", + "at most 2 relays per /24 network)\n", sep = "")) +ggsave("fast-exits.png", width = 8, height = 6, dpi = 100) + +s <- data +s <- s[s$min_rate == 11875 & s$ports == "80-443-554-1755", ] +s <- aggregate(list(relays = s$relays, P_exit = 100 * s$exit_prob), + by = list(date = as.Date(cut.Date(as.Date(s$valid_after), DETAIL)), + min_advbw = s$min_advbw), FUN = median) +s <- melt(s, id.vars = c("date", "min_advbw")) +s <- data.frame(s, type = ifelse(s$variable == "P_exit", + "Total exit probability (in %)", "Number of relays")) +ggplot(s, aes(x = date, y = value, colour = as.factor(min_advbw))) + +geom_line() + +facet_grid(type ~ ., scales = "free_y") + +scale_x_date(name = "") + +scale_y_continuous(name = "") + +scale_colour_manual(name = "Advertised bandwidth (in KB/s)", + values = brewer.pal(6, "Purples")[2:6]) + +opts(title = paste("Relays meeting the fast-exit requirements\nwith", + "varying minimum advertised bandwidth capacities"), + legend.position = "top") +ggsave("advertised-bandwidths-requirement.png", width = 8, height = 6, + dpi = 100) + +t <- data +t1 <- t[t$min_rate == 11875 & t$ports == "80-443-554-1755" & + t$min_advbw == 5000, ] +t2 <- t[t$min_rate == 10000 & t$ports == "80-443" & + t$min_advbw == 2000, ] +t <- rbind(data.frame(t1, var = "95+ Mbit/s, 5000+ KB/s, 80/443/554/1755"), + data.frame(t2, var = "80+ Mbit/s, 2000+ KB/s, 80/443")) +t <- aggregate(list(relays = t$relays, P_exit = 100 * t$exit_prob), + by = list(date = as.Date(cut.Date(as.Date(t$valid_after), DETAIL)), + var = t$var), FUN = median) +t <- melt(t, id.vars = c("date", "var")) +t <- data.frame(t, type = ifelse(t$variable == "P_exit", + "Total exit probability (in %)", "Number of relays")) +ggplot(t, aes(x = date, y = value, colour = var)) + +geom_line(size = 0.75) + +facet_grid(type ~ ., scales = "free_y") + +scale_x_date(name = "") + +scale_y_continuous(name = "") + +scale_colour_manual(name = "", values = c("purple", "orange")) + +opts(title = "Relays almost meeting the fast-exit requirements", + legend.position = "top") +ggsave("almost-fast-exits.png", width = 8, height = 6, dpi = 100) +