[tor-commits] [tech-reports/master] Add unchanged performance roadmap sources.

karsten at torproject.org karsten at torproject.org
Fri Aug 31 10:35:38 UTC 2012


commit 01f6a9eb4e3142c8014c00add2bb392638b55802
Author: Karsten Loesing <karsten.loesing at gmx.net>
Date:   Thu Aug 30 11:48:58 2012 +0200

    Add unchanged performance roadmap sources.
    
    svn co https://svn.torproject.org/svn/projects/performance/
---
 2009/performance/.gitignore                        |   12 +
 2009/performance/Makefile                          |   40 +
 2009/performance/equilibrium.R                     |  156 ++
 2009/performance/extensiontimes.pdf                |  609 +++++++
 2009/performance/node-selection/exit-capacity.R    |   24 +
 2009/performance/node-selection/exit-capacity.dat  |   14 +
 2009/performance/node-selection/hillclimbing.py    |  252 +++
 .../node-selection/marginal-bandwidth.R            |   33 +
 2009/performance/node-selection/node-bw.dat        | 1044 ++++++++++++
 2009/performance/node-selection/opt_0.1.pickle.dat | 1033 ++++++++++++
 .../performance/node-selection/opt_0.25.pickle.dat | 1033 ++++++++++++
 2009/performance/node-selection/opt_0.5.pickle.dat | 1033 ++++++++++++
 .../performance/node-selection/opt_0.75.pickle.dat | 1033 ++++++++++++
 .../performance/node-selection/opt_1e-1.pickle.dat | 1033 ++++++++++++
 .../performance/node-selection/opt_1e-3.pickle.dat | 1033 ++++++++++++
 .../performance/node-selection/opt_1e-6.pickle.dat | 1033 ++++++++++++
 2009/performance/node-selection/opt_tor.pickle.dat | 1033 ++++++++++++
 .../node-selection/plot-node-selection.R           |   63 +
 .../performance/node-selection/vary-network-load.R |   93 +
 2009/performance/perf-todo                         |  170 ++
 2009/performance/performance.bib                   |  203 +++
 2009/performance/performance.tex                   | 1775 ++++++++++++++++++++
 2009/performance/prettyref.sty                     |   37 +
 23 files changed, 12789 insertions(+), 0 deletions(-)

diff --git a/2009/performance/.gitignore b/2009/performance/.gitignore
new file mode 100644
index 0000000..e116aa1
--- /dev/null
+++ b/2009/performance/.gitignore
@@ -0,0 +1,12 @@
+equilibrium.Rout
+equilibrium.pdf
+node-selection/exit-capacity.Rout
+node-selection/exit-capacity.pdf
+node-selection/optimum-selection-probabilities.pdf
+node-selection/plot-node-selection.Rout
+node-selection/relative-selection-probabilities.pdf
+node-selection/vary-network-load.Rout
+node-selection/vary-network-load.pdf
+performance.pdf
+performance.toc
+
diff --git a/2009/performance/Makefile b/2009/performance/Makefile
new file mode 100644
index 0000000..05a5973
--- /dev/null
+++ b/2009/performance/Makefile
@@ -0,0 +1,40 @@
+LATEX=pdflatex
+
+all: performance.pdf
+
+performance.pdf: node-selection/exit-capacity.pdf \
+	node-selection/optimum-selection-probabilities.pdf \
+	node-selection/relative-selection-probabilities.pdf \
+	node-selection/vary-network-load.pdf \
+	equilibrium.pdf \
+	performance.bbl
+
+%.pdf %.aux: %.tex
+	rm -f $*.ps $*.pdf
+	$(LATEX) $< || { rm -f $*.pdf $*.dvi $*.aux $*.idx && false ; }
+	while grep 'Rerun to get cross-references right.' $*.log ; do \
+	  $(LATEX) $< || { rm -f $*.pdf $*.dvi $*.aux $*.idx && false ; } ; done
+
+%.bbl: %.bib %.aux
+	bibtex $* || { rm -f $*.pdf $*.dvi $*.aux $*.bbl && false ; }
+
+node-selection/optimum-selection-probabilities.pdf \
+node-selection/relative-selection-probabilities.pdf: node-selection/plot-node-selection.R
+	cd node-selection; R CMD BATCH --vanilla ../$<
+
+node-selection/%.pdf: node-selection/%.R
+	cd node-selection; R CMD BATCH --vanilla ../$<
+
+%.pdf: %.R
+	R CMD BATCH --vanilla $<
+
+hillclimbing:
+	cd node-selection; ./hillclimbing.py node-bw.dat
+
+clean:
+	rm -f *~ \
+	      *.Rout \
+	      *.aux *.log *.out *.bbl *.blg *.pdf
+	rm -f node-selection/*.pdf node-selection/*.Rout  node-selection/*~
+
+.PHONY: all hillclimbing clean
diff --git a/2009/performance/equilibrium.R b/2009/performance/equilibrium.R
new file mode 100644
index 0000000..0395191
--- /dev/null
+++ b/2009/performance/equilibrium.R
@@ -0,0 +1,156 @@
+###
+### Draw graph showing relationship between network bandwidth and
+### performance, by treating the system as a market for throughput.
+###
+### See related blog post for the details:
+###  http://www.lightbluetouchpaper.org/2007/07/18/economics-of-tor-performance/
+###
+### Steven J. Murdoch <http://www.cl.cam.ac.uk/~sjm217/>, 2007-07-18
+###
+
+###
+### Function declarations
+###
+
+### Plot and annotate a point at the intersection of two curves
+plotIntersect <- function(x, y1=NULL, y2=NULL, i=NULL, ann=NULL, adj=NULL) {
+  if (is.null(i))
+    iIntersect <- which.min(abs(y1-y2))
+  else
+    iIntersect <- i
+  
+  points(x[iIntersect], y1[iIntersect], pch=20, col="darkred")
+
+  if (!is.null(ann))
+    text(x[iIntersect], y1[iIntersect], ann, adj=adj)
+    
+  return(iIntersect)
+}
+
+###
+### Customization
+###
+
+## Number of Tor users (vague estimate)
+uUsersTotal <- 1e5
+
+## Bandwidth (bits/sec) of the Tor network (from
+## http://www.noreply.org/tor-running-routers/) as of July 2007
+rBandwidthTotal <- ( 120     # 120 MByte/sec
+                     * 1e6   # convert to byte/sec
+                     * 8 )   # convert to bit/sec
+
+## Range for plotting number of users
+rgUsers <- c(uUsersTotal* 0.4, uUsersTotal * 1.6)
+
+## Number of points to plot on X axis
+cUserPoints <- 300
+
+###
+### Generate curves
+###
+
+## Number of users
+uUsers <- seq(rgUsers[1], rgUsers[2], length.out=cUserPoints)
+
+## Supply curve: (bandwidth per user) * (number of users) = rBandwidthTotal
+rSupply <- rBandwidthTotal / uUsers
+
+## Supply curve, for a 50% increase in total bandwidth
+rBandwidthTotalNew <- rBandwidthTotal * 1.5
+rSupplyNew <- rBandwidthTotalNew / uUsers
+
+## Demand curve (arbitrary values, just something that looks OK)
+rDemandB <- 5e-12*(uUsers^3) + 6000
+
+## Second demand curve
+rDemandC <-25*(uUsers-20000)^0.5
+## Find intersect between rDemandB and rSupply
+iIntersectOrig <- which.min(abs(rSupply-rDemandB))
+## Find how much rDemandC needs to be shifted to intersect at same point
+rDiff <- rSupply[iIntersectOrig] - rDemandC[iIntersectOrig]
+## Shift curve
+rDemandC <- rDemandC + rDiff
+## Find user count at intersection
+uIntersectOrig <- uUsers[iIntersectOrig]
+
+###
+### Plotting
+###
+
+## Color declarations (selected from colour-blind-safe pallete)
+colDemand <- "#336600"
+colSupply <- "#9900CC"
+
+## Open output file (increase size, so it can be scaled down later and
+## anti-aliased)
+RequestedSize <- Sys.getenv("IMAGESIZE")
+if (RequestedSize=="") {
+  ImageSize <- c(440, 290)
+} else {
+  ImageSize <- as.numeric(strsplit(RequestedSize, split="x")[[1]])
+}
+
+ScaleFactor <- 2
+ImageSize <- ImageSize * ScaleFactor
+pdf("equilibrium.pdf")
+
+## Style declarations
+par(mar=c(2.2,2.2,0.1,0.1))
+par(lwd=3)
+par(col="black")
+
+## Define plotting area and plot supply curve
+plot(uUsers, rSupply, ann=FALSE, frame.plot=FALSE, type="l", col=colSupply, axes=FALSE)
+title(xlab = "Number of users", line=1)
+title(ylab = "Throughput per user", line=1)
+
+## Draw other curves
+lines(uUsers, rSupplyNew, col=colSupply)
+lines(uUsers, rDemandB, col=colDemand)
+lines(uUsers, rDemandC, col=colDemand)
+lines(rep(uIntersectOrig, 2), c(6376.615, 24055.385), col=colDemand)
+
+## Plot and annotate intersection
+iIntersectA <- plotIntersect(uUsers, rSupplyNew, i=iIntersectOrig, ann="A", adj=c(-1.2,0.1))
+iIntersectB <- plotIntersect(uUsers, rSupplyNew, y2=rDemandB, ann="B", adj=c(-2,0.3))
+iIntersectC <- plotIntersect(uUsers, rSupplyNew, y2=rDemandC, ann="C", adj=c(0.5,1.8))
+
+## Arrow showing movement of supply curve
+arrows(60023.34, 17409.23, 73314.21, 18406.15, lwd=1)
+
+## Hide values (since they are basically guesswork and not important anyway)
+axis(1, par("usr")[1:2], label=FALSE)
+axis(2, par("usr")[3:4], label=FALSE)
+
+## Labels
+text(41634.54, 23942.4, "Supply", adj=c(-0.1,0.5))
+text(137844.4, 18810.24, "Demand", adj=c(-0.2,0.5))
+
+## Save output file
+dev.off()
+
+###
+### Collect some stats
+###
+
+## Original values
+uOrig <- uUsers[iIntersectOrig]
+rOrig <- rSupply[iIntersectOrig]
+print(sprintf("Original users: %.0f, bandwidth %.0f", uOrig, rOrig))
+
+## Percentage change
+print(sprintf("Intersect A users: %.0f%%, bandwidth %.0f%%",
+              uUsers[iIntersectA]/uOrig*100, rSupplyNew[iIntersectA]/rOrig*100))
+print(sprintf("Intersect B users: %.0f%%, bandwidth %.0f%%",
+              uUsers[iIntersectB]/uOrig*100, rSupplyNew[iIntersectB]/rOrig*100))
+print(sprintf("Intersect C users: %.0f%%, bandwidth %.0f%%",
+              uUsers[iIntersectC]/uOrig*100, rSupplyNew[iIntersectC]/rOrig*100))
+
+###
+### Old intersection was 94180 users, 10193 bps each
+### Intersection A (constant demand) is 100% of old users, and 150% of bandwidth
+### Intersection B is 118% of old users and 127% of bandwidth
+### Intersection C is 133% of old users and 113% of bandwidth
+###
+
diff --git a/2009/performance/extensiontimes.pdf b/2009/performance/extensiontimes.pdf
new file mode 100644
index 0000000..5b35caf
--- /dev/null
+++ b/2009/performance/extensiontimes.pdf
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diff --git a/2009/performance/node-selection/exit-capacity.R b/2009/performance/node-selection/exit-capacity.R
new file mode 100644
index 0000000..ece84e9
--- /dev/null
+++ b/2009/performance/node-selection/exit-capacity.R
@@ -0,0 +1,24 @@
+## 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()
diff --git a/2009/performance/node-selection/exit-capacity.dat b/2009/performance/node-selection/exit-capacity.dat
new file mode 100644
index 0000000..a23f8cf
--- /dev/null
+++ b/2009/performance/node-selection/exit-capacity.dat
@@ -0,0 +1,14 @@
+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
diff --git a/2009/performance/node-selection/hillclimbing.py b/2009/performance/node-selection/hillclimbing.py
new file mode 100755
index 0000000..f294e46
--- /dev/null
+++ b/2009/performance/node-selection/hillclimbing.py
@@ -0,0 +1,252 @@
+#!/usr/bin/python
+
+### Use a simple hillclimbing algorithm to find the selection
+### probability vector for Tor nodes which minimizes the expected
+### latency. Takes as input a file, one node per line, specifying
+### bandwidth capacity and usage, both in bytes/sec, space separated.
+
+### Copyright (c) 2008-2009, Steven J. Murdoch
+### 
+### Redistribution and use in source and binary forms, with or without
+### modification, are permitted provided that the following conditions are
+### met:
+### 
+###     * Redistributions of source code must retain the above copyright
+### notice, this list of conditions and the following disclaimer.
+### 
+###     * Redistributions in binary form must reproduce the above
+### copyright notice, this list of conditions and the following disclaimer
+### in the documentation and/or other materials provided with the
+### distribution.
+### 
+###     * Neither the names of the copyright owners nor the names of its
+### contributors may be used to endorse or promote products derived from
+### this software without specific prior written permission.
+### 
+### THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+### "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+### LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+### A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+### OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+### SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+### LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+### DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+### THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+### (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+### OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import sys
+
+if __name__=="__main__":
+    import matplotlib
+    matplotlib.use('TkAgg') # do this before importing pylab
+
+import matplotlib.pyplot as plt
+from numpy import *
+
+import time
+
+import threading
+
+## Number of iterations before narrowing adjustment
+ILIMIT = 1000
+## Maximum number of iterations before exiting
+NUM=8000
+## Fraction of network capacity which is being used (set to None to
+## use actual network data
+USAGE=0.5
+
+def wait(x, p, L, isbroken):
+    '''Calculate waiting time at each node (assuming M/D/1 queue)'''
+    z = p*L*x
+    a = z*x
+    b = 2.0*(1.0-z)
+    return x + a/b
+
+def calc_waittime(prob, xs, totalusage, debug=False):
+    '''Function to optimize'''
+    #print "info", prob[0], nodebw[0], totalusage
+    ## Check that probabilities really add up to 1
+    assert abs(1.0 - prob.sum()) < 1e-6
+
+    ## Find overloaded nodes
+    loading_factor = xs*prob*totalusage
+    broken = (loading_factor < 0.0) | (loading_factor > 1.0)
+    if broken.any():
+        return None
+    ## Number of broken nodes
+    #print "Broken", len([x for x in broken if x])
+
+    ## Calculate weighted waiting time
+    wtime = wait(xs, prob, totalusage, False)
+    wtime[broken] = -1.0
+
+    ## Get maximimum waiting time for non-broken nodes
+    cap = wtime.max()
+
+    ## Calculate capped weighted waiting time
+    wtime[broken] = cap
+    wwtime = wtime * prob
+    
+    return wwtime
+
+def load_data(fn):
+    fh = file(fn, "rt")
+
+    ## Load in node bandwidths and total network usage
+    totalusage = float64(0.0)
+    totalbw = 0.0
+    nodebw = []
+    for line in fh:
+        bw, usage = line.split()
+        bw = float(bw)/512.0
+        usage = float(usage)/512.0
+        ## Ignore nodes with BW outside acceptable range
+        if bw<=0.0 or bw >= (10.0e6/512.0):
+            continue
+        totalusage += usage
+        totalbw += bw
+        nodebw.append(bw)
+
+    # Node selection probabilities
+    pu = array([1.0/len(nodebw)] * len(nodebw)) # uniform
+    pt = array([bw / totalbw for bw in nodebw]) # Tor
+
+    anodebw = array(nodebw)
+    xs = 1.0/anodebw
+
+    ## Weighted waiting time for Tor and uniform weighting
+    x = calc_waittime(pt, xs, totalusage, True)
+    y = calc_waittime(pu, xs, totalusage, True)
+
+    print "E(Tor)", sum(x)
+    print "E(Uniform)", sum(y)
+
+    return totalusage, anodebw, x,y
+
+def optimize(sprob, ss, xs, totalusage, amount):
+    l = len(sprob)
+    i = 0
+    while True:
+        i += 1
+        ## Take copy of base
+        prob = sprob.copy()
+        
+        ## Find elements to change
+        a = random.randint(0, l)
+        b = random.randint(0, l)
+
+        ## Clip amount to modify by
+        if prob[a] < amount:
+            damount = prob[a]
+        else:
+            damount = amount
+
+        ## Move between two elements
+        prob[a] -= damount
+        prob[b] += damount
+
+        ## Calculate the function to optimize
+        wwait = calc_waittime(prob, xs, totalusage, True)
+        if wwait is None:
+            ## We reached a bad state, give up
+            continue
+
+        ## If this is an improvement, return new probabilities
+        s = wwait.sum()
+        if s < ss:
+            return i, prob, wwait, s
+        if i > ILIMIT:
+            ## We reached the maximum limit, return the original parameters
+            wwait = calc_waittime(sprob, xs, totalusage, True)
+            return i, sprob, wwait, wwait.sum()
+
+def save(anodebw, prob, xs, totalusage, count):
+    fh = file("nodeprob-%06d"%count, "wt")
+    anodebw.dump(fh)
+    prob.dump(fh)
+    xs.dump(fh)
+    totalusage.dump(fh)
+    fh.close()
+    #fh.write("bw prob\n")
+    #for i in range(len(anodebw)):
+    #    fh.write("%f %f\n"%(anodebw[i],prob[i]))
+    #fh.close()
+
+class Animator(threading.Thread):
+    def __init__(self, prob, totalusage, anodebw, fig, ax):
+        threading.Thread.__init__(self)
+        self.prob = prob
+        self.totalusage = totalusage
+        self.anodebw = anodebw
+        self.xs = 1.0/anodebw
+
+        self.anodebw = anodebw
+        self.fig = fig
+        self.ax = ax
+    
+    def run(self):
+        wwait = calc_waittime(self.prob, self.xs, self.totalusage)
+        s = wwait.sum()
+        #line, = self.ax.plot(self.anodebw, wwait.cumsum())
+        line, = self.ax.plot(wwait.cumsum())
+        amount = 1.0/2
+
+        ## Main optimization loop
+        i = 0
+        while i < NUM:
+            i += 1
+            cnt, self.prob, wwait, s = optimize(self.prob, s, self.xs, self.totalusage, amount)
+            print "%6d %4d %f"%(i, cnt, s)
+            if cnt > ILIMIT:
+                ## We tried for too long to optimize so reduce the
+                ## amount to modify probabilities
+                amount /= 2.0
+                print "Narrowing... to", amount
+                save(self.anodebw, self.prob, self.xs, self.totalusage, i)
+                
+            line.set_ydata(cumsum(wwait))
+            self.fig.canvas.draw()
+        ## Save the intermediate result
+        save(self.anodebw, self.prob, self.xs, self.totalusage, i)
+        sys.exit()
+
+def main():
+    if len(sys.argv) != 2:
+        print "Usage: hillclimbing.py FILE"
+        sys.exit()
+
+    ## Load data file, where each line is:
+    ##  BANDWIDTH USAGE
+    ## Where both are in kB/s
+    fn = sys.argv[1]
+    totalusage, anodebw, x, y = load_data(fn)
+
+    ## Try for different levels of bandwidth usage
+    #totalusage = anodebw.sum()*(1-1e-6)
+    #totalusage = anodebw.sum()*(1-1e-3)
+    #totalusage = anodebw.sum()*(1-1e-1)
+    #totalusage = anodebw.sum()*(0.75)
+    #totalusage = anodebw.sum()*(0.5)
+    #totalusage = anodebw.sum()*(0.25)
+    if USAGE != None:
+        totalusage = anodebw.sum()*USAGE
+    
+    pt = anodebw/anodebw.sum()
+    
+    plt.ioff()
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+
+    ## Optimize selection probabilities
+    anim = Animator(pt, totalusage, anodebw, fig, ax)
+
+    win = fig.canvas.manager.window
+    fig.canvas.manager.window.after(100, anim.start)
+
+    plt.show()
+
+if __name__=="__main__":
+    main()
+
+# vim: ai ts=4 sts=4 et sw=4
diff --git a/2009/performance/node-selection/marginal-bandwidth.R b/2009/performance/node-selection/marginal-bandwidth.R
new file mode 100644
index 0000000..2b00c77
--- /dev/null
+++ b/2009/performance/node-selection/marginal-bandwidth.R
@@ -0,0 +1,33 @@
+## Load in data files
+tt <- read.table("opt_tor.pickle.dat", header=TRUE)
+
+## Node bandwidth
+bw <- tt$bw
+
+## Calculate network capcity
+capacity <- sum(bw)
+
+## Calculate selection probabilties
+torProb <- bw/capacity
+optProb <- tt$prob
+
+## Calculate bandwidth given to each user,
+##  assuming 1 million users
+torShare <- bw/(torProb*1e6)
+optShare <- bw/(optProb*1e6)
+
+## Output graph
+col <- rainbow(8)
+ylim <- range(c(torShare, optShare), finite=TRUE)
+pdf("marginal-bandwidth.pdf")
+plot(bw, torShare, ylim=ylim, ylab="Bandwidth per user (cells/s)",
+     log="y", type="l", xlab="Node bandwidth (cells/s)",
+     main="Comparison of bandwidth at each node,\nfor Tor and Optimized weighting algorithms",
+     frame.plot=FALSE, lwd=2)
+lines(bw, optShare, type="b", col=col[8])
+
+par(xpd=NA)
+text(x=rep(max(bw), 2), y=c(torShare[length(bw)], optShare[length(bw)]), pos=3, c("Tor", "Optimized"))
+
+## Close output device
+dev.off()
diff --git a/2009/performance/node-selection/node-bw.dat b/2009/performance/node-selection/node-bw.dat
new file mode 100644
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diff --git a/2009/performance/node-selection/opt_1e-3.pickle.dat b/2009/performance/node-selection/opt_1e-3.pickle.dat
new file mode 100644
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diff --git a/2009/performance/node-selection/opt_1e-6.pickle.dat b/2009/performance/node-selection/opt_1e-6.pickle.dat
new file mode 100644
index 0000000..ae46599
--- /dev/null
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diff --git a/2009/performance/node-selection/plot-node-selection.R b/2009/performance/node-selection/plot-node-selection.R
new file mode 100644
index 0000000..b302e6a
--- /dev/null
+++ b/2009/performance/node-selection/plot-node-selection.R
@@ -0,0 +1,63 @@
+## 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$bw), rep(0,2), lty=2)
+
+title(main="Selection probabilility compared to Tor")
+x <- rep(8111.669, 4)
+y <- c(1.396915e-02, 4.962766e-03, 1.635106e-03, 7.446809e-06)
+par(xpd=TRUE)
+text(x,y,c("50%", "75%", "90%", ">99%"), adj=c(0,0.5))
+dev.off()
diff --git a/2009/performance/node-selection/vary-network-load.R b/2009/performance/node-selection/vary-network-load.R
new file mode 100644
index 0000000..e2b96dd
--- /dev/null
+++ b/2009/performance/node-selection/vary-network-load.R
@@ -0,0 +1,93 @@
+## The waiting time for a node (assuming no overloaded nodes)
+## x: 1/bandwidth
+## q: selection probability
+## L: network load
+wait <- function(x,q,L) {
+  a <- q*L*x*x
+  b <- 2*(1-q*x*L)
+  return (x + a/b)
+}
+
+## The weighted wait time
+wwait <- function(x,q,L) {
+  return (q*wait(x,q,L))
+}
+
+## Average latency, returning NA for infinite
+netLatency <- function(x, q, L) {
+  if (any(x*q*L <0 | x*q*L >1)) {
+    return (NA)
+  } else {
+    return (sum(wwait(x, q, L)))
+  }
+}
+
+## Load in data files
+t1 <- read.table("opt_1e-6.pickle.dat", header=TRUE)
+t2 <- read.table("opt_1e-3.pickle.dat", header=TRUE)
+t3 <- read.table("opt_1e-1.pickle.dat", header=TRUE)
+t4 <- read.table("opt_0.75.pickle.dat", header=TRUE)
+t5 <- read.table("opt_0.5.pickle.dat", header=TRUE)
+t6 <- read.table("opt_0.25.pickle.dat", header=TRUE)
+t7 <- read.table("opt_0.1.pickle.dat", header=TRUE)
+tt <- read.table("opt_tor.pickle.dat", header=TRUE)
+
+## Node bandwidth and reciprocal
+bw <- t1$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()
diff --git a/2009/performance/perf-todo b/2009/performance/perf-todo
new file mode 100644
index 0000000..b02f690
--- /dev/null
+++ b/2009/performance/perf-todo
@@ -0,0 +1,170 @@
+
+performance roadmap (elaboration of sec 3.1 from the 3-year roadmap)
+
+Coderman:
+  - 1.1, UDP-Tor.
+    - Explore how hard it would be to get a user-space TCP stack with
+      suitable properties.
+    - Help Ian and Chris deploy a testbed (prototype) network
+
+Roger, Steven:
+  - 1.2, new circuit window sizes
+    - Conclude whether the transition period will hurt as much as it
+      seems like it will.
+    * Pick a lower number, and switch to it.
+Metrics: gather queue sizes from relays so we have a better sense of
+what's actually going on.
+
+Roger, others:
+  * 2.1, squeeze loud circuits
+    - Evaluate the code to see what stats we can keep about circuit use.
+    - Write proposals for various meddling. Look at the research papers
+      that Juliusz pointed us to. Ask our systems friends. Plan to put
+      a lot of the parameters in the consensus, so we can tune it with
+      short turnaround times.
+
+  - 2.4, rate-limit at clients
+    - Consider ways to choose what rate limits to use.
+    - Reconsider in the context of 2.1 proposals above
+
+  - 2.5, Default exit policies
+    * Change Vidalia's default exit policy to not click "other protocols".
+    D let exit relays specify some destination networks/ports that get
+      rate limited further.
+Metrics: At what fraction of exit relays allowing a given port out
+do connections to that port start to suffer? That is, if even 5%
+of the relays (by bandwidth) allowing a port to exit are enough for
+most connections to that port to work fine, then we're going to have
+a tough time pushing unwanted traffic off the network just by changing
+some exit policies. (Alas, this question is messy because it pretends
+that the amount of traffic generated for port x is independent of x.
+How to phrase it so it's more useful?)
+
+  - 2.6, tell users not to file-share
+    * Put statement on the Tor front page
+    * Put statement on the download pages too
+    * And the FAQ
+    D Should we put some sort of popup in Vidalia? How to detect?
+    - Contact Azureus people and get them to fix their docs, and/or
+      remove the feature, and/or pop up a warning.
+
+  - 3.1.2, Tor weather
+    * Link to it from the Tor relay page
+    * and the torrc.sample
+M   - Put a link in Vidalia's relay interface
+I   * Implement time-to-notification (immediate, a day, a week)
+N   - Build a plan for how Tor weather can learn about hibernating
+      relays if we take them out of the v3 consensus and we obsolete v2.
+
+Steven, with help from Jake
+  - 3.1.3, facebook app
+    - ? [Steven should fill this in]
+
+  - 3.6, incentives to relay
+    - Sort out how to do circuit priority in practice. I think the only
+      answer here is to make different TLS connections for different
+      priorities. (Otherwise other people can free-ride on your
+      high-priority conns.)
+Metrics: what period of time should the gold star status last? That is,
+What period of time, taken as a rolling snapshot of which relays are
+present in the network, guarantees a sufficiently large anonymity set
+for high-priority relays?
+    D design the actual crypto for certs, and build something.
+
+  - 3.7, clients automatically become relays
+    D what's the algorithm for knowing when you should upgrade from
+      being a client to a bridge, and from being a bridge to a relay?
+    D implement enough internal performance/stability tracking for
+      clients to be able to know when they've crossed a threshold.
+    (both of these only really doable once we've done more work on
+     looking at anonymity risks)
+
+  - 4.1, balance traffic better
+    * Steven and Mike should decide if we should do Steven's plan
+      (rejigger the bandwidth numbers at the authorities based on
+      Steven's algorithm), or Mike's plan (relay scanning to identify
+      the unbalanced relays and fix them on the fly), or both.
+    * Figure out how to actually modify bandwidths in the consensus. We
+      may need to change the consensus voting algorithm to decide what
+      bandwidth to advertise based on something other than median:
+      if 7 authorities provide bandwidths, and 2 are doing scanning,
+      then the 5 that aren't scanning will outvote any changes. Should
+      all 7 scan? Should only some vote? Extra points if it doesn't
+      change all the numbers every new consensus, so consensus diffing
+      is still practical.
+    - Make clients actually use the bandwidth numbers in the consensus.
+    - Should a relay with rate/burst of 100/100 have the same capacity
+      as a relay with rate/burst of 100/500?
+
+  - 4.2, getting better bandwidth estimates
+Metrics: how accurate are the ten-second-bandwidth-burst advertised
+numbers anyway, in terms of guessing capacity? Steven says we're at 50%
+load, but is that just because our advertised bandwidth is a function
+of our recent load?
+    - What is "true" capacity anyway?
+Metrics: What other algorithms can we use to produce a more accurate
+advertised bandwidth?
+    - Compare Mike's active probe data to the capacities. Where there
+      are differences, which one is less wrong?
+    - We should instrument Tor relays to compute peer bandwidths. But
+      to do that, we need to understand the anonymity implications of
+      publishing all this aggregated traffic data. Is it really safe
+      enough?
+
+  - 4.3, Micah's latency plan
+    - Hear some numbers from him about how good it can be and how
+      reliable it can be, in theory.
+    - Micah writes a proposal to make Tor relays compute their
+      coordinates, so we can do more direct measurements of whether it
+      should work.
+
+  - 4.4, Looking at exit policy when picking relays
+    D pending results from section 4.1
+
+  - 4.5, Older entry guards are overloaded
+Metrics: compare "how fast each relay should be based on its advertised
+capacity" with "how long the relay has had the guard flag", to see how
+big an issue this is really.
+Metrics: How many relays does a client touch over time x, given that they
+drop old guards y seconds after choosing them? Even if y is infinite,
+we have some number based on guards going away. How does x grow as we
+reduce y?
+    * Pick a conservative y like six months, and implement.
+    D Reduce y based on the results of the metrics. (don't partition
+      clients too far by tor version though.)
+Metrics: if we were more flexible in our Guard stability criteria, how
+many more relays would get the Guard flag? How would that influence the
+above numbers? I'd like to become very flexible so more than half of
+the relays get to be guards. Are there cutoffs that are reasonable and
+fit naturally into the data from the past few years?
+Metrics: if we're more flexible in our Guard speed criteria, how does
+that impact the speed that clients should expect? Originally we avoided
+20KB/s relays as guards, because "then clients can't ever get more than
+20KB/s". But they can't get that now anyway.
+    - Write a proposal for what new criteria we should use, and why
+      that'll safely increase the guard set.
+
+  - 5.1, better round-robin
+
+
+  - 5.2, better timeouts for giving up on circuits/streams
+    * clients gather data about circuit timeouts, and then abandon
+      circuits that take more than a std dev above that.
+Metrics: Right now we abandon the circuit after 10 seconds for the first
+try. What are the download stats if we don't abandon it? What "try a
+new one" timeouts will minimize the number of circuits we go through,
+while also minimizing the time-until-user-gets-website?
+    - Change Tor so it launches a new stream attempt on schedule, but
+      is willing to use the original attempt if it completes earlier.
+    D same with stream timeouts.
+
+  - 5.3, try extending a few other places
+
+  - 5.4, bundle the first data cell with the begin cell
+
+  - 6.1, directory overhead
+    * deploy proposal 158, the "microdescriptor" plan
+
+  - 6.2, TLS overhead
+    D can wait
+
diff --git a/2009/performance/performance.bib b/2009/performance/performance.bib
new file mode 100644
index 0000000..4090376
--- /dev/null
+++ b/2009/performance/performance.bib
@@ -0,0 +1,203 @@
+ at InProceedings{tuneup,
+  author =       {Robin Snader and Nikita Borisov},
+  title =        {A Tune-up for {Tor}: Improving Security and Performance in the {Tor} Network},
+  booktitle = {Network \& Distributed System Security Symposium},
+  year =         {2008},
+  month =        {February},
+  publisher = {Internet Society},
+}
+
+ at inproceedings{mccoy-pet2008,
+  title = {Shining Light in Dark Places: Understanding the {Tor} Network}, 
+  author = {Damon McCoy and Kevin Bauer and Dirk Grunwald and Tadayoshi Kohno and Douglas
+        Sicker}, 
+  booktitle = {Proceedings of the Eighth International Symposium on Privacy Enhancing
+        Technologies (PETS 2008)}, 
+