and two more things to move

svn:r18564

doc/design-paper/node-selection/exit-capacity.R

-## Read data
-t <- read.table("exit-capacity.dat", header=TRUE)
-
-## Normalize columns
-t[,2] <- t[,2]/max(t[,2])*100
-t[,3] <- t[,3]/max(t[,3])*100
-
-## Remove uninteresting ports
-ports <- c(22, 25, 80, 119, 135, 443,
-           563, 8080, 6667)
-t <- t[t$port %in% ports,]
-
-## Plot
-pdf("exit-capacity.pdf")
-par(las=1)
-col <- grey(c(1,4)/5)
-barplot(t(as.matrix(t[,2:3])), names=t$port,
-        beside=TRUE, xlab="Port number",
-        ylab="Exit capacity available (%)",
-        col=col, cex.axis=0.8, cex.names=0.8)
-par(xpd=TRUE)
-legend(x="topright", legend=c("Nodes", "Bandwidth"),
-       fill=col, bty="n", inset=c(-0.05,-0.15))
-dev.off()

doc/design-paper/node-selection/exit-capacity.dat

-port numExits totalBandwidth
-21 115 39282983
-22 116 41341901
-25 5 1530934
-80 315 60290055
-119 16 13026366
-135 7 1709809
-443 326 60956345
-445 8 1730289
-563 102 24056338
-1314 307 55315711
-4661 7 1648369
-8080 307 55318361
-6667 116 41961767

doc/design-paper/node-selection/plot-node-selection.R

-## Load in data files
-t1 = read.table("opt_1e-6.pickle.dat", header=TRUE)
-t2 = read.table("opt_1e-3.pickle.dat", header=TRUE)
-t3 = read.table("opt_1e-1.pickle.dat", header=TRUE)
-t4 = read.table("opt_0.75.pickle.dat", header=TRUE)
-t5 = read.table("opt_0.5.pickle.dat", header=TRUE)
-t6 = read.table("opt_0.25.pickle.dat", header=TRUE)
-t7 = read.table("opt_0.1.pickle.dat", header=TRUE)
-tt = read.table("opt_tor.pickle.dat", header=TRUE)
-
-## Calculate selection probabilties that Tor uses
-o = t1$bw/sum(t1$bw)
-
-#plot(t1$bw, cumsum(t1$prob), col="red", type="l")
-#lines(t1$bw, cumsum(t2$prob), col="pink")
-#lines(t1$bw, cumsum(t3$prob), col="blue")
-#lines(t1$bw, cumsum(t4$prob), col="orange")
-#lines(t1$bw, cumsum(t5$prob), col="purple")
-#lines(t1$bw, cumsum(tt$prob))
-
-## Plot probabiltieis
-pdf("optimum-selection-probabilities.pdf")
-col <- rainbow(8)
- plot(t1$bw, t1$prob, col=col[1], type="b", ylim=c(0,0.035),xlab="Bandwidth (cells/s)",
-      ylab="Selection probability", frame.plot=FALSE)
-lines(t1$bw, t2$prob, col=col[2], type="b")
-lines(t1$bw, t3$prob, col=col[3], type="b")
-lines(t1$bw, t4$prob, col=col[4], type="b")
-lines(t1$bw, t5$prob, col=col[5], type="b")
-
-## These are too messy
-##lines(t1$bw, t6$prob, col=col[6], type="b")
-##lines(t1$bw, t7$prob, col=col[7], type="b")
-
-lines(t1$bw, tt$prob,col=col[8], type="b")
-lines(t1$bw, o, type="l", lwd=2)
-
-## Annotate graph
-title(main="Optimum node selection probability")
-x <- rep(8254.383, 4)
-y <- c(0.03453717, 0.02553347, 0.02219589, 0.02048830)
-par(xpd=TRUE)
-text(x,y,c("50%", "75%", "90%", ">99%"), adj=c(0,0.5))
-dev.off()
-
-## Plot probabilities relative to what Tor does
-pdf("relative-selection-probabilities.pdf")
- plot(t1$bw, t1$prob-o, col=col[1], type="b", xlab="Bandwidth (cells/s)",
-      ylab="Selection probability - Tor's selection probability", frame.plot=FALSE, ylim=c(-0.002,0.015))
-lines(t1$bw, t2$prob-o, col=col[2], type="b")
-lines(t1$bw, t3$prob-o, col=col[3], type="b")
-lines(t1$bw, t4$prob-o, col=col[4], type="b")
-lines(t1$bw, t5$prob-o, col=col[5], type="b")
-lines(t1$bw, tt$prob-o,col=col[8], type="b")
-
-lines(range(t1$bw), rep(0,2), lty=2)
-
-title(main="Selection probabilility compared to Tor")
-x <- rep(8111.669, 4)
-y <- c(1.396915e-02, 4.962766e-03, 1.635106e-03, 7.446809e-06)
-par(xpd=TRUE)
-text(x,y,c("50%", "75%", "90%", ">99%"), adj=c(0,0.5))
-dev.off()

doc/design-paper/node-selection/vary-network-load.R

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

doc/design-paper/prettyref.sty

-%%
-%% This is file `prettyref.sty',
-%% generated with the docstrip utility.
-%%
-%% The original source files were:
-%%
-%% prettyref.dtx  (with options: `style')
-%% 
-%% Copyright (c) 1995 Kevin Ruland
-%% 
-%%
-%% prettyref v3.0
-%%
-%% Copyright 1995,1998.  by Kevin Ruland kevin@rodin.wustl.edu
-%%
-\ProvidesPackage{prettyref}[1998/07/09 v3.0]
-\def\newrefformat#1#2{%
-  \@namedef{pr@#1}##1{#2}}
-\newrefformat{eq}{\textup{(\ref{#1})}}
-\newrefformat{lem}{Lemma \ref{#1}}
-\newrefformat{thm}{Theorem \ref{#1}}
-\newrefformat{cha}{Chapter \ref{#1}}
-\newrefformat{sec}{Section \ref{#1}}
-\newrefformat{tab}{Table \ref{#1} on page \pageref{#1}}
-\newrefformat{fig}{Figure \ref{#1} on page \pageref{#1}}
-\def\prettyref#1{\@prettyref#1:}
-\def\@prettyref#1:#2:{%
-  \expandafter\ifx\csname pr@#1\endcsname\relax%
-    \PackageWarning{prettyref}{Reference format #1\space undefined}%
-    \ref{#1:#2}%
-  \else%
-    \csname pr@#1\endcsname{#1:#2}%
-  \fi%
-}
-\endinput
-%%
-%% End of file `prettyref.sty'.