mirror of
https://gitlab.torproject.org/tpo/core/tor.git
synced 2024-11-10 21:23:58 +01:00
94 lines
2.4 KiB
R
94 lines
2.4 KiB
R
## The waiting time for a node (assuming no overloaded nodes)
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## x: 1/bandwidth
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## q: selection probability
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## L: network load
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wait <- function(x,q,L) {
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a <- q*L*x*x
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b <- 2*(1-q*x*L)
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return (x + a/b)
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}
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## The weighted wait time
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wwait <- function(x,q,L) {
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return (q*wait(x,q,L))
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}
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## Average latency, returning NA for infinite
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netLatency <- function(x, q, L) {
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if (any(x*q*L <0 | x*q*L >1)) {
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return (NA)
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} else {
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return (sum(wwait(x, q, L)))
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}
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}
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## Load in data files
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t1 <- read.table("opt_1e-6.pickle.dat", header=TRUE)
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t2 <- read.table("opt_1e-3.pickle.dat", header=TRUE)
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t3 <- read.table("opt_1e-1.pickle.dat", header=TRUE)
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t4 <- read.table("opt_0.75.pickle.dat", header=TRUE)
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t5 <- read.table("opt_0.5.pickle.dat", header=TRUE)
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t6 <- read.table("opt_0.25.pickle.dat", header=TRUE)
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t7 <- read.table("opt_0.1.pickle.dat", header=TRUE)
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tt <- read.table("opt_tor.pickle.dat", header=TRUE)
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## Node bandwidth and reciprocal
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bw <- t1$bw
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x <- 1/bw
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## Calculate network capcity
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capacity <- sum(bw)
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## Calculate selection probabilties that Tor uses
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torProb <- bw/sum(bw)
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## Load values to try
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varyLoad <- seq(0.01,0.93,0.01)
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latencyTor <- c()
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latency3 <- c()
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latency4 <- c()
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latency5 <- c()
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for (L in varyLoad) {
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latencyTor <- append(latencyTor,
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netLatency(x, torProb, capacity*L))
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latency3 <- append(latency3,
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netLatency(x, t3$prob, capacity*L))
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latency4 <- append(latency4,
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netLatency(x, t4$prob, capacity*L))
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latency5 <- append(latency5,
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netLatency(x, t5$prob, capacity*L))
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}
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## Output graph
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pdf("vary-network-load.pdf")
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## Set up axes
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yFac <- 1000
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xFac <- 100
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ylim <- range(na.omit(c(latencyTor, latency3, latency4, latency5)))
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ylim <- c(0,0.015) * yFac
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xlim <- c(0,1) * xFac
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plot(NA, NA,
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xlim=xlim, ylim=ylim,
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frame.plot=FALSE,
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xlab = "Network load (%)",
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ylab = "Average queuing delay (ms)",
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main = "Latency for varying network loads")
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## Plot data
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col <- rainbow(8)
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lines(varyLoad*xFac, latency3*yFac, col=col[3])
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lines(varyLoad*xFac, latency4*yFac, col=col[4])
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lines(varyLoad*xFac, latency5*yFac, col=col[5])
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lines(varyLoad*xFac, latencyTor*yFac)
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## Plot points at which selection probabilities are optimal
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par(xpd=TRUE)
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points(c(0.9, 0.75, 0.5, 1)*xFac, rep(par("usr")[3], 4),
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col=c(col[3:5], "black"), pch=20,
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cex=2)
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## Close output device
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dev.off()
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