Update Tor conf

2019-11-27 16:08:44 +01:00 · 2019-11-27 16:08:44 +01:00 · a319c3d9be
parent a3af4ffc3b
commit a319c3d9be
4 changed files with 223 additions and 21 deletions
--- a/r/config_light.R
+++ b/r/config_light.R
@ -46,7 +46,7 @@ f$strat <- revalue(f$strat, c("1"="T", "0"="D"))
 f$fast_count <- as.factor(f$fast_count)
 f$percentile <-factor(f$percentile,levels=c("MAX", "P99.9", "P99", "P75", "P50", "P25", "P1", "P0.1", "MIN"))
 f <- f %>% mutate (lat = latency / 1000)
-gf <- ggplot(sqldf("select * from f where percentile != 'MAX'  and percentile != 'D1' and percentile != 'D0.1'") %>% arrange(percentile), aes(x=strat:fast_count,y=lat,group=percentile,fill=percentile)) +
+gf <- ggplot(sqldf("select * from f where percentile != 'MAX'  and percentile != 'P1' and percentile != 'P0.1'") %>% arrange(percentile), aes(x=strat:fast_count,y=lat,group=percentile,fill=percentile)) +
  geom_bar(stat='identity', position='identity') +
  scale_fill_grey() +
  geom_hline(yintercept = 200) +
@ -57,7 +57,7 @@ gf <- ggplot(sqldf("select * from f where percentile != 'MAX'  and percentile !=
  labs(fill="Percentile") +
  theme_classic()

-plot_grid(gd, ge, gf, ncol=1) + ggsave("light_config.png", dpi=300, dev='png', height=17, width=15, units="cm")
+plot_grid(gd, ge, gf, ncol=1) + ggsave("light_config.pdf", dpi=300, dev='pdf', height=17, width=15, units="cm")

 g <- read.csv("../../donar-res/tmp_light/guards.csv")
 g$strat <- as.factor(g$strat)
@ -75,7 +75,204 @@ gg <- ggplot(sqldf("select * from g where percentile != 'MAX' and percentile !=
  coord_cartesian(ylim=c(0,600)) +
  ylab("Latency (ms)") +
  xlab("Strategy:Guards") +
+  labs(fill="Percentile") +
  theme_classic()

 gg + ggsave("light_guards.png", dpi=300, dev='png', height=7, width=15, units="cm")

+h <- read.csv("../../donar-res/tmp_light/complem.csv")
+h$strat <- revalue(h$strat, c("ticktock"="T", "duplicate"="D"))
+h$percentile <-factor(h$percentile,levels=c("MAX", "P99.9", "P99", "P75", "P50", "P25", "P1", "P0.1", "MIN"))
+h$mode <- factor(h$mode, levels=c("no_redundancy", "no_scheduler", "donar"))
+h$mode <- revalue(h$mode, c("no_redundancy" = "scheduler", "no_scheduler" = "padding", "donar"="both"))
+h <- h %>% mutate (lat = latency / 1000)
+gh <- ggplot(sqldf("select * from h where percentile != 'MAX' and percentile != 'P1' and percentile != 'P0.1' ") %>% arrange(percentile), aes(x=strat:mode,y=lat,group=percentile,fill=percentile)) +
+  geom_bar(stat='identity', position='identity') +
+  scale_fill_grey() +
+  geom_hline(yintercept = 200) +
+  geom_hline(yintercept = 400) +
+  coord_cartesian(ylim=c(0,500)) +
+  ylab("Latency (ms)") +
+  xlab("Strategy:Feature") +
+  labs(fill="Percentile") +
+  theme_classic()
+gh + ggsave("light_complementary.png", dpi=300, dev='png', height=7, width=15, units="cm")
+
+i <- read.csv("../../donar-res/tmp_light/battle.csv")
+i$percentile <- revalue(i$percentile, c("MAX" = "100%", "P99.9"="99.9%", "P99"="99%", "P75" = "75%", "P50"="50%","P25"="25%","P1"="1%","P0.1"="0.1%","MIN"="0%"))
+i$percentile <-factor(i$percentile,levels=c("100%", "99.9%", "99%", "75%", "50%", "25%", "1%", "0.1%", "0%"))
+i <- i %>% mutate (lat = latency / 1000)
+i$secmode <- factor(i$secmode, levels=c("hardened", "default", "light"))
+i$algo <- factor(i$algo, levels=c("simple", "dup2", "lightning-ticktock", "lightning-dup"))
+i$algo <- revalue(i$algo, c("dup2"="torfone", "simple"="simple", "lightning-ticktock"="donar-ticktock", "lightning-dup"="donar-dup"))
+gi <- ggplot(sqldf("select * from i where percentile != '100%' and percentile != '1%' and percentile != '0.1%' and secmode = 'default' ") %>% arrange(percentile), aes(x=algo,y=lat,group=percentile,fill=percentile)) +
+  geom_bar(stat='identity', position='identity',width=1) +
+  #scale_fill_grey() +
+  scale_y_continuous(expand = c(0, 0)) +
+  scale_x_discrete(expand = c(0, 0)) +
+  scale_fill_viridis_d() +
+  geom_hline(yintercept = 200) +
+  geom_hline(yintercept = 400) +
+  coord_cartesian(ylim=c(0,600)) +
+  ylab("Latency (ms)") +
+  xlab("Algorithm") +
+  labs(fill="Distribution") +#, title="linear scale, zoomed") +
+  theme_classic() +
+  theme(axis.text.x = element_text(angle = 10, hjust=1), plot.margin = unit(c(0.3,0.2,0.2,1), "cm"))
+gibis <- ggplot(sqldf("select * from i where percentile != 'MAX' and percentile != 'P1' and percentile != 'P0.1' ") %>% arrange(percentile), aes(x=algo:secmode,y=lat,group=percentile,fill=percentile)) +
+  geom_bar(stat='identity', position='identity') +
+  scale_fill_grey() +
+  scale_y_log10() +
+  #geom_hline(yintercept = 200) +
+  #geom_hline(yintercept = 400) +
+  coord_cartesian(ylim=c(100,250000)) +
+  ylab("Latency (ms)") +
+  xlab("Algorithm:Security Profile") +
+  labs(fill="Percentile", title="log scale, full") +
+  theme_classic() +
+  theme(axis.text.x = element_text(angle = 20, hjust=1), plot.margin = unit(c(0.2,0.2,0.2,1), "cm"))
+  
+plot_grid(gibis, gi, ncol=1) + ggsave("light_battle.png", dpi=300, dev='png', height=12, width=15, units="cm")
+
+gi + ggsave("battle_color.pdf", dpi=300, dev='pdf', height=7, width=12, units='cm')
+
+fastprobe = read.csv(text=
+"
+group,pad,count,strat
+fast,orig,0.47348781884198304,ticktock
+fast,pad,0.2629428441715532,ticktock
+probe,orig,0.23705662669600178,ticktock
+probe,pad,0.026512710290461965,ticktock
+probe,orig,0.1154165656941915,duplicate
+probe,pad,0.015345347029610245,duplicate
+fast,orig,0.7349205324574037,duplicate
+fast,pad,0.13431755481879454,duplicate
+")
+gj <- ggplot(fastprobe, aes(x=group:pad, y=count, fill=strat)) +
+  geom_bar(stat='identity', position='dodge') +
+  scale_fill_grey() +
+  ylab("Count") +
+  xlab("Group:Padding") +
+  labs(fill="Strategy") +
+  scale_y_continuous(labels = scales::percent) +
+  theme_classic() +
+  theme(axis.text.x = element_text(angle = 20, hjust=1))
+gj + ggsave("light_fastprobe.png", dpi=300, dev='png', height=7, width=15, units="cm")
+
+fastlinks = read.csv(text=
+"
+link_count,occ_count,strat
+5,0,ticktock
+9,0.20634920634920634,ticktock
+8,0.25396825396825395,ticktock
+7,0.1111111111111111,ticktock
+6,0.1746031746031746,ticktock
+10,0.1746031746031746,ticktock
+11,0.07936507936507936,ticktock
+12,0,ticktock
+9,0.19047619047619047,duplicate
+8,0.14285714285714285,duplicate
+10,0.20634920634920634,duplicate
+7,0.09523809523809523,duplicate
+6,0.09523809523809523,duplicate
+11,0.14285714285714285,duplicate
+5,0.015873015873015872,duplicate
+12,0.1111111111111111,duplicate
+")
+gk <- ggplot(fastlinks, aes(x=link_count, y=occ_count, fill=strat)) +
+  geom_bar(stat='identity', position='dodge') +
+  scale_fill_grey() +
+  ylab("Runs") +
+  xlab("Links fast at least once") +
+  labs(fill="Strategy") +
+  scale_y_continuous(labels = scales::percent) +
+  theme_classic()
+  #theme(axis.text.x = element_text(angle = 20, hjust=1))
+gk + ggsave("light_fastlinks.png", dpi=300, dev='png', height=7, width=15, units="cm")
+
+
+
+l <- read.csv("../../donar-res/tmp_light/basic.csv")
+l <- l %>% mutate (lat = latency / 1000)
+gl <- ggplot(sqldf("select * from l where way='client'"), aes(x=packet_id,y=lat)) +
+  coord_cartesian(ylim=c(0,500)) +
+  geom_hline(yintercept = 200) +
+  geom_hline(yintercept = 400) +
+  xlab("Packet Identifier") +
+  ylab("Latency (ms)") +
+  labs(linetype="Way") +
+  geom_point() +
+  theme_classic()
+gl + ggsave("light_single.png", dpi=300, dev='png', height=7, width=15, units="cm")
+
+m <- read.csv("../../donar-res/tmp_light/fp.csv")
+m <- m %>% mutate (lat = latency / 1000)
+gm <- ggplot(sqldf("select * from m where `way`='client'"), aes(x=packet_id,y=lat,color=group)) +
+  scale_color_grey() +
+  coord_cartesian(ylim=c(0,500)) +
+  geom_hline(yintercept = 200) +
+  geom_hline(yintercept = 400) +
+  xlab("Packet Identifier") +
+  ylab("Latency (ms)") +
+  labs(color="Group") +
+  labs(linetype="Way") +
+  geom_point() +
+  theme_classic()
+
+n <- read.csv("../../donar-res/tmp_light/linkgroup.csv")
+n$group <- revalue(n$group, c("fast" = "fast link", "probe" = "other link"))
+gn <- ggplot(n, aes(x=packet_id, y=link_id, color=group)) +
+  scale_color_grey() +
+  #scale_color_viridis_d() +
+  xlab("Packet Identifier") +
+  ylab("Link Identifier") +
+  labs(color="Group") +
+  geom_point() +
+  theme_classic()
+
+plot_grid(gl, gm, gn, ncol=1) + ggsave("light_single.png", dpi=300, dev='png', height=17, width=15, units="cm")
+
+gn+ggsave("links_group.pdf", dpi=300, dev='pdf', height=7, width=15, units="cm")
+
+o <- read.csv("../../donar-res/tmp_light/torrate.csv")
+o <- o %>% mutate (latency = latency / 1000)
+o <- o %>% mutate (rate = round(rate, 0))
+
+q1coefs <- coef(lm(latency ~ rate, data = sqldf("select rate, latency from o where percentile= 'P25'")))
+mcoefs <- coef(lm(latency ~ rate, data = sqldf("select rate, latency from o where percentile= 'P50'")))
+q3coefs <- coef(lm(latency ~ rate, data = sqldf("select rate, latency from o where percentile= 'P75'")))
+
+o$percentile <- revalue(o$percentile, c("MAX"="100%", "MIN" = "0%", "P99.9" = "99.9%", "P99" = "99%", "P75" = "75%", "P50" = "50%", "P25" = "25%"))
+o$percentile <-factor(o$percentile,levels=c("100%", "99.9%", "99%", "75%", "50%", "25%", "1%", "0.1%", "0%"))
+o$rate <- factor(o$rate)
+
+go <- ggplot(sqldf("select * from o where percentile != 'MAX' and percentile != 'P1' and percentile != 'P0.1'") %>% arrange(percentile), aes(x=rate,y=latency,group=percentile,fill=percentile)) +
+  geom_bar(stat='identity', position='identity', width=1) +
+  #scale_fill_grey() +
+  scale_fill_viridis_d(expand = c(0, 0)) +
+  scale_y_continuous(expand = c(0, 0)) +
+  scale_x_discrete(expand = c(0, 0)) +
+  ylab("Latency (ms)") +
+  xlab("Packets / second") +
+  labs(fill="Distribution") +#, title="low values") +
+  coord_cartesian(ylim=c(0,500)) +
+  geom_hline(yintercept = 200) +
+  geom_hline(yintercept = 400) +
+  #geom_abline(intercept = q1coefs[1], slope = q1coefs[2], linetype='dashed') +
+  #geom_abline(intercept = mcoefs[1], slope = mcoefs[2], linetype='dashed') +
+  #geom_abline(intercept = q3coefs[1], slope = q3coefs[2], linetype='dashed') +
+  theme_classic()
+gp <- ggplot(sqldf("select * from o where percentile != 'MAX' and percentile != 'P1' and percentile != 'P0.1'") %>% arrange(percentile), aes(x=rate,y=latency,group=percentile,fill=percentile)) +
+  geom_bar(stat='identity', position='identity') +
+  scale_fill_grey() +
+  scale_y_log10() +
+  ylab("Latency (ms)") +
+  xlab("Packets / second") +
+  labs(fill="Percentile", title="high values (log scale)") +
+  coord_cartesian(ylim=c(100,100000)) +
+  geom_hline(yintercept = 200) +
+  geom_hline(yintercept = 400) +
+  theme_classic()
+plot_grid(gp, go, align = "v", axis = "l", ncol=1) + ggsave("torrate.pdf", dpi=300, dev='pdf', height=12, width=15, units="cm")
+
+go + ggsave("torrate_scale.pdf", dpi=300, dev='pdf', height=6, width=12, units='cm')
--- a/r/thunder_configure.R
+++ b/r/thunder_configure.R
@ -281,25 +281,25 @@ tor_lat_stack <- tor_multi_lat %>%
    dplyr::group_by(run,conf) %>%
    dplyr::summarise(
      id = paste(first(run),first(conf)),
-      min = min(latency), 
-      q25 = quantile(latency,0.25) -  min(latency), 
-      median = median(latency) - quantile(latency,0.25),
-      q75 = quantile(latency,0.75) - median(latency),
-      q95 = quantile(latency,0.95) - quantile(latency,0.75),
-      q99 = quantile(latency,0.99) - quantile(latency,0.95),
-      max = max(latency) - quantile(latency,0.99), 
+      P0 = min(latency), 
+      P25 = quantile(latency,0.25) -  min(latency), 
+      P50 = median(latency) - quantile(latency,0.25),
+      P75 = quantile(latency,0.75) - median(latency),
+      P95 = quantile(latency,0.95) - quantile(latency,0.75),
+      P99 = quantile(latency,0.99) - quantile(latency,0.95),
+      P100 = max(latency) - quantile(latency,0.99), 
      max_sort = max(latency), 
      median_sort = median(latency)
    )

-tor_lat_stack <- gather(tor_lat_stack, 'min', 'max', 'q25', 'median', 'q75', 'q95', 'q99', key="quantile_name", value="quantile_value")
+tor_lat_stack <- gather(tor_lat_stack, 'P0', 'P100', 'P25', 'P50', 'P75', 'P95', 'P99', key="quantile_name", value="quantile_value")
 v13 <- ggplot(tor_lat_stack, aes(
  x=reorder(id,median_sort),
  y=quantile_value,
-  fill=factor(quantile_name, levels=c('max','q99','q95','q75', 'median', 'q25', 'min')))
+  fill=factor(quantile_name, levels=c('P100','P99','P95','P75', 'P50', 'P25', 'P0')))
 ) +
  coord_cartesian(ylim = c(0,1500)) +
-  labs(fill="quantile")+
+  labs(fill="Percentile")+
  xlab("Tor circuits") +
  ylab("RTT (ms)") +
  geom_bar(stat="identity", position="stack",width=1) +
@ -310,10 +310,10 @@ v13 <- ggplot(tor_lat_stack, aes(
 v14 <- ggplot(tor_lat_stack, aes(
  x=reorder(id,median_sort),
  y=quantile_value,
-  fill=factor(quantile_name, levels=c('max','q99','q95','q75', 'median', 'q25', 'min')))
+  fill=factor(quantile_name, levels=c('P100','P99','P95','P75', 'P50', 'P25', 'P0')))
 ) +
  #coord_cartesian(ylim = c(0,1500)) +
-  labs(fill="quantile")+
+  labs(fill="Percentile")+
  xlab("Tor circuits") +
  ylab("RTT (ms)") +
  geom_bar(stat="identity", position="stack",width=1) +
@ -324,10 +324,10 @@ v14 <- ggplot(tor_lat_stack, aes(
 v15 <- ggplot(tor_lat_stack, aes(
  x=reorder(id,max_sort),
  y=quantile_value,
-  fill=factor(quantile_name, levels=c('max','q99','q95','q75', 'median', 'q25', 'min')))
+  fill=factor(quantile_name, levels=c('P100','P99','P95','P75', 'P50', 'P25', 'P0')))
 ) +
  coord_cartesian(ylim = c(0,1500)) +
-  labs(fill="quantile")+
+  labs(fill="Percentile")+
  xlab("Tor circuits") +
  ylab("RTT (ms)") +
  geom_bar(stat="identity", position="stack",width=1) +
@ -338,10 +338,10 @@ v15 <- ggplot(tor_lat_stack, aes(
 v16 <- ggplot(tor_lat_stack, aes(
  x=reorder(id,max_sort),
  y=quantile_value,
-  fill=factor(quantile_name, levels=c('max','q99','q95','q75', 'median', 'q25', 'min')))
+  fill=factor(quantile_name, levels=c('P100','P99','P95','P75', 'P50', 'P25', 'P0')))
 ) +
  #coord_cartesian(ylim = c(0,1500)) +
-  labs(fill="quantile")+
+  labs(fill="Percentile")+
  xlab("Tor circuits") +
  ylab("RTT (ms)") +
  geom_bar(stat="identity", position="stack",width=1) +
@ -349,8 +349,8 @@ v16 <- ggplot(tor_lat_stack, aes(
  theme_classic() +
  theme(axis.text.x=element_blank(), axis.ticks.x = element_blank(), legend.key.size = unit(0.2, "cm"),plot.tag.position='bottom')

-t4 <- plot_grid(v16, v15, v14, v13, labels = c('A', 'B', 'C', 'D'), ncol=1)
-t4 + ggsave("tor_30ms.png", dpi=300, dev='png', height=20, width=15, units="cm")
+t4 <- plot_grid(v16, v15, v14, v13, align = "v", axis = "l", labels = c('A', 'B', 'C', 'D'), ncol=1)
+t4 + ggsave("tor_30ms.pdf", dpi=150, dev='pdf', height=15, width=12, units="cm")


 tor_lat_stack_100 <- tor_multi_lat_100 %>%
--- a/src/meas_lat.c
+++ b/src/meas_lat.c
@ -1,4 +1,4 @@
-#include <stdlib.h>
+  #include <stdlib.h>
 #include <stdio.h>
 #include <sys/timerfd.h>
 #include <time.h>
--- a/5
+++ b/5
@ -1,4 +1,9 @@
 ControlPort 9051
+
 UseEntryGuards 0
+
 SafeLogging 0
+
+IsolateDestPort 1
+
 #Log INFO stdout