tor_multipath_voip/r/config_light.R

library(ggplot2)
library(sqldf)
library(plyr)
library(dplyr)
library(cowplot)

d <- read.csv("../../donar-res/tmp_light/window3.csv")
d <- d %>% mutate (window = window / 1000)
d$strat <- as.factor(d$strat)
d$window <- as.factor(d$window)
#d$decile <- factor(d$decile, levels = c("MIN", "D0.1", "D1", "D25", "D50", "D75", "D99", "D99.9"))
d$percentile <-factor(d$percentile,levels=c("MAX", "P99.9", "P99", "P75", "P50", "P25", "P1", "P0.1", "MIN"))
d$strat <- revalue(d$strat, c("1"="T", "0"="D"))
d <- d %>% mutate (lat = latency / 1000)
gd <- ggplot(sqldf("select * from d where percentile != 'MAX' and percentile != 'P1' and percentile != 'P0.1'") %>% arrange(percentile), aes(x=strat:window,y=lat,group=percentile,fill=percentile)) +
  geom_bar(stat='identity', position='identity') +
  scale_fill_grey() +
  ylab("Latency (ms)") +
  xlab("Strategy:Window (sec)") +
  labs(fill="Percentile") +
  coord_cartesian(ylim=c(0,600)) +
  geom_hline(yintercept = 200) +
  geom_hline(yintercept = 400) +
  theme_classic()

e <- read.csv("../../donar-res/tmp_light/links3.csv")
e$strat <- as.factor(e$strat)
e$links <- as.factor(e$links)
e$strat <- revalue(e$strat, c("1"="T", "0"="D"))
e$percentile <-factor(e$percentile,levels=c("MAX", "P99.9", "P99", "P75", "P50", "P25", "P1", "P0.1", "MIN"))
e <- e %>% mutate (lat = latency / 1000)
ge <- ggplot(sqldf("select * from e where percentile != 'MAX'  and percentile != 'P1' and percentile != 'P0.1' and links != 6 and links != 4") %>% arrange(percentile), aes(x=strat:links,y=lat,group=percentile,fill=percentile)) +
  geom_bar(stat='identity', position='identity') +
  scale_fill_grey() +
  ylab("Latency (ms)") +
  xlab("Strategy:Links") +
  labs(fill="Percentile") +
  geom_hline(yintercept = 200) +
  geom_hline(yintercept = 400) +
  coord_cartesian(ylim=c(0,600)) +
  theme_classic()

f <- read.csv("../../donar-res/tmp_light/fast3.csv")
f$strat <- as.factor(f$strat)
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 != '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) +
  geom_hline(yintercept = 400) +
  coord_cartesian(ylim=c(0,600)) +
  ylab("Latency (ms)") +
  xlab("Strategy:Fast Links") +
  labs(fill="Percentile") +
  theme_classic()

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)
g$strat <- revalue(g$strat, c("1"="T", "0"="D"))
g$guard <- as.factor(g$guard)
g$guard <- revalue(g$guard, c("guard_1"="1", "guard_3"="3", "guard_5"="5", "guard_7"="7", "guard_9"="9", "guard_11"="11", "simple"="inf"))
g$guard <- factor(g$guard, levels=c("1", "3", "5", "7", "9", "11", "inf"))
g$percentile <-factor(g$percentile,levels=c("MAX", "P99.9", "P99", "P75", "P50", "P25", "P1", "P0.1", "MIN"))
g <- g %>% mutate (lat = latency / 1000)
gg <- ggplot(sqldf("select * from g where percentile != 'MAX' and percentile != 'P1' and percentile != 'P0.1' ") %>% arrange(percentile), aes(x=strat:guard,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(0,600)) +
  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')