279 lines
12 KiB
R
279 lines
12 KiB
R
library(ggplot2)
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library(sqldf)
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library(plyr)
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library(dplyr)
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library(cowplot)
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d <- read.csv("../../donar-res/tmp_light/window3.csv")
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d <- d %>% mutate (window = window / 1000)
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d$strat <- as.factor(d$strat)
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d$window <- as.factor(d$window)
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#d$decile <- factor(d$decile, levels = c("MIN", "D0.1", "D1", "D25", "D50", "D75", "D99", "D99.9"))
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d$percentile <-factor(d$percentile,levels=c("MAX", "P99.9", "P99", "P75", "P50", "P25", "P1", "P0.1", "MIN"))
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d$strat <- revalue(d$strat, c("1"="T", "0"="D"))
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d <- d %>% mutate (lat = latency / 1000)
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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)) +
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geom_bar(stat='identity', position='identity') +
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scale_fill_grey() +
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ylab("Latency (ms)") +
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xlab("Strategy:Window (sec)") +
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labs(fill="Percentile") +
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coord_cartesian(ylim=c(0,600)) +
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geom_hline(yintercept = 200) +
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geom_hline(yintercept = 400) +
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theme_classic()
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e <- read.csv("../../donar-res/tmp_light/links3.csv")
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e$strat <- as.factor(e$strat)
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e$links <- as.factor(e$links)
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e$strat <- revalue(e$strat, c("1"="T", "0"="D"))
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e$percentile <-factor(e$percentile,levels=c("MAX", "P99.9", "P99", "P75", "P50", "P25", "P1", "P0.1", "MIN"))
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e <- e %>% mutate (lat = latency / 1000)
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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)) +
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geom_bar(stat='identity', position='identity') +
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scale_fill_grey() +
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ylab("Latency (ms)") +
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xlab("Strategy:Links") +
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labs(fill="Percentile") +
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geom_hline(yintercept = 200) +
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geom_hline(yintercept = 400) +
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coord_cartesian(ylim=c(0,600)) +
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theme_classic()
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f <- read.csv("../../donar-res/tmp_light/fast3.csv")
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f$strat <- as.factor(f$strat)
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f$strat <- revalue(f$strat, c("1"="T", "0"="D"))
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f$fast_count <- as.factor(f$fast_count)
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f$percentile <-factor(f$percentile,levels=c("MAX", "P99.9", "P99", "P75", "P50", "P25", "P1", "P0.1", "MIN"))
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f <- f %>% mutate (lat = latency / 1000)
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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)) +
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geom_bar(stat='identity', position='identity') +
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scale_fill_grey() +
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geom_hline(yintercept = 200) +
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geom_hline(yintercept = 400) +
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coord_cartesian(ylim=c(0,600)) +
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ylab("Latency (ms)") +
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xlab("Strategy:Fast Links") +
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labs(fill="Percentile") +
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theme_classic()
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plot_grid(gd, ge, gf, ncol=1) + ggsave("light_config.pdf", dpi=300, dev='pdf', height=17, width=15, units="cm")
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g <- read.csv("../../donar-res/tmp_light/guards.csv")
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g$strat <- as.factor(g$strat)
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g$strat <- revalue(g$strat, c("1"="T", "0"="D"))
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g$guard <- as.factor(g$guard)
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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"))
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g$guard <- factor(g$guard, levels=c("1", "3", "5", "7", "9", "11", "inf"))
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g$percentile <-factor(g$percentile,levels=c("MAX", "P99.9", "P99", "P75", "P50", "P25", "P1", "P0.1", "MIN"))
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g <- g %>% mutate (lat = latency / 1000)
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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)) +
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geom_bar(stat='identity', position='identity') +
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scale_fill_grey() +
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geom_hline(yintercept = 200) +
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geom_hline(yintercept = 400) +
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coord_cartesian(ylim=c(0,600)) +
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ylab("Latency (ms)") +
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xlab("Strategy:Guards") +
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labs(fill="Percentile") +
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theme_classic()
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gg + ggsave("light_guards.png", dpi=300, dev='png', height=7, width=15, units="cm")
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h <- read.csv("../../donar-res/tmp_light/complem.csv")
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h$strat <- revalue(h$strat, c("ticktock"="T", "duplicate"="D"))
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h$percentile <-factor(h$percentile,levels=c("MAX", "P99.9", "P99", "P75", "P50", "P25", "P1", "P0.1", "MIN"))
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h$mode <- factor(h$mode, levels=c("no_redundancy", "no_scheduler", "donar"))
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h$mode <- revalue(h$mode, c("no_redundancy" = "scheduler", "no_scheduler" = "padding", "donar"="both"))
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h <- h %>% mutate (lat = latency / 1000)
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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)) +
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geom_bar(stat='identity', position='identity') +
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scale_fill_grey() +
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geom_hline(yintercept = 200) +
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geom_hline(yintercept = 400) +
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coord_cartesian(ylim=c(0,500)) +
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ylab("Latency (ms)") +
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xlab("Strategy:Feature") +
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labs(fill="Percentile") +
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theme_classic()
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gh + ggsave("light_complementary.png", dpi=300, dev='png', height=7, width=15, units="cm")
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i <- read.csv("../../donar-res/tmp_light/battle.csv")
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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%"))
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i$percentile <-factor(i$percentile,levels=c("100%", "99.9%", "99%", "75%", "50%", "25%", "1%", "0.1%", "0%"))
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i <- i %>% mutate (lat = latency / 1000)
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i$secmode <- factor(i$secmode, levels=c("hardened", "default", "light"))
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i$algo <- factor(i$algo, levels=c("simple", "dup2", "lightning-ticktock", "lightning-dup"))
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i$algo <- revalue(i$algo, c("dup2"="torfone", "simple"="simple", "lightning-ticktock"="donar-ticktock", "lightning-dup"="donar-dup"))
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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)) +
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geom_bar(stat='identity', position='identity',width=1) +
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#scale_fill_grey() +
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scale_y_continuous(expand = c(0, 0)) +
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scale_x_discrete(expand = c(0, 0)) +
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scale_fill_viridis_d() +
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geom_hline(yintercept = 200) +
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geom_hline(yintercept = 400) +
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coord_cartesian(ylim=c(0,600)) +
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ylab("Latency (ms)") +
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xlab("Algorithm") +
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labs(fill="Distribution") +#, title="linear scale, zoomed") +
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theme_classic() +
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theme(axis.text.x = element_text(angle = 10, hjust=1), plot.margin = unit(c(0.3,0.2,0.2,1), "cm"))
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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)) +
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geom_bar(stat='identity', position='identity') +
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scale_fill_grey() +
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scale_y_log10() +
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#geom_hline(yintercept = 200) +
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#geom_hline(yintercept = 400) +
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coord_cartesian(ylim=c(100,250000)) +
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ylab("Latency (ms)") +
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xlab("Algorithm:Security Profile") +
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labs(fill="Percentile", title="log scale, full") +
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theme_classic() +
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theme(axis.text.x = element_text(angle = 20, hjust=1), plot.margin = unit(c(0.2,0.2,0.2,1), "cm"))
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plot_grid(gibis, gi, ncol=1) + ggsave("light_battle.png", dpi=300, dev='png', height=12, width=15, units="cm")
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gi + ggsave("battle_color.pdf", dpi=300, dev='pdf', height=7, width=12, units='cm')
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fastprobe = read.csv(text=
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"
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group,pad,count,strat
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fast,orig,0.47348781884198304,ticktock
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fast,pad,0.2629428441715532,ticktock
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probe,orig,0.23705662669600178,ticktock
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probe,pad,0.026512710290461965,ticktock
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probe,orig,0.1154165656941915,duplicate
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probe,pad,0.015345347029610245,duplicate
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fast,orig,0.7349205324574037,duplicate
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fast,pad,0.13431755481879454,duplicate
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")
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gj <- ggplot(fastprobe, aes(x=group:pad, y=count, fill=strat)) +
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geom_bar(stat='identity', position='dodge') +
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scale_fill_grey() +
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ylab("Count") +
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xlab("Group:Padding") +
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labs(fill="Strategy") +
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scale_y_continuous(labels = scales::percent) +
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theme_classic() +
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theme(axis.text.x = element_text(angle = 20, hjust=1))
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gj + ggsave("light_fastprobe.png", dpi=300, dev='png', height=7, width=15, units="cm")
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fastlinks = read.csv(text=
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"
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link_count,occ_count,strat
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5,0,ticktock
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9,0.20634920634920634,ticktock
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8,0.25396825396825395,ticktock
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7,0.1111111111111111,ticktock
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6,0.1746031746031746,ticktock
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10,0.1746031746031746,ticktock
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11,0.07936507936507936,ticktock
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12,0,ticktock
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9,0.19047619047619047,duplicate
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8,0.14285714285714285,duplicate
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10,0.20634920634920634,duplicate
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7,0.09523809523809523,duplicate
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6,0.09523809523809523,duplicate
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11,0.14285714285714285,duplicate
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5,0.015873015873015872,duplicate
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12,0.1111111111111111,duplicate
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")
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gk <- ggplot(fastlinks, aes(x=link_count, y=occ_count, fill=strat)) +
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geom_bar(stat='identity', position='dodge') +
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scale_fill_grey() +
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ylab("Runs") +
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xlab("Links fast at least once") +
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labs(fill="Strategy") +
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scale_y_continuous(labels = scales::percent) +
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theme_classic()
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#theme(axis.text.x = element_text(angle = 20, hjust=1))
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gk + ggsave("light_fastlinks.png", dpi=300, dev='png', height=7, width=15, units="cm")
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l <- read.csv("../../donar-res/tmp_light/basic.csv")
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l <- l %>% mutate (lat = latency / 1000)
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gl <- ggplot(sqldf("select * from l where way='client'"), aes(x=packet_id,y=lat)) +
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coord_cartesian(ylim=c(0,500)) +
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geom_hline(yintercept = 200) +
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geom_hline(yintercept = 400) +
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xlab("Packet Identifier") +
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ylab("Latency (ms)") +
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labs(linetype="Way") +
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geom_point() +
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theme_classic()
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gl + ggsave("light_single.png", dpi=300, dev='png', height=7, width=15, units="cm")
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m <- read.csv("../../donar-res/tmp_light/fp.csv")
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m <- m %>% mutate (lat = latency / 1000)
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gm <- ggplot(sqldf("select * from m where `way`='client'"), aes(x=packet_id,y=lat,color=group)) +
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scale_color_grey() +
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coord_cartesian(ylim=c(0,500)) +
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geom_hline(yintercept = 200) +
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geom_hline(yintercept = 400) +
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xlab("Packet Identifier") +
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ylab("Latency (ms)") +
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labs(color="Group") +
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labs(linetype="Way") +
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geom_point() +
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theme_classic()
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n <- read.csv("../../donar-res/tmp_light/linkgroup.csv")
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n$group <- revalue(n$group, c("fast" = "fast link", "probe" = "other link"))
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gn <- ggplot(n, aes(x=packet_id, y=link_id, color=group)) +
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scale_color_grey() +
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#scale_color_viridis_d() +
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xlab("Packet Identifier") +
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ylab("Link Identifier") +
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labs(color="Group") +
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geom_point() +
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theme_classic()
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plot_grid(gl, gm, gn, ncol=1) + ggsave("light_single.png", dpi=300, dev='png', height=17, width=15, units="cm")
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gn+ggsave("links_group.pdf", dpi=300, dev='pdf', height=7, width=15, units="cm")
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o <- read.csv("../../donar-res/tmp_light/torrate.csv")
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o <- o %>% mutate (latency = latency / 1000)
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o <- o %>% mutate (rate = round(rate, 0))
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q1coefs <- coef(lm(latency ~ rate, data = sqldf("select rate, latency from o where percentile= 'P25'")))
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mcoefs <- coef(lm(latency ~ rate, data = sqldf("select rate, latency from o where percentile= 'P50'")))
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q3coefs <- coef(lm(latency ~ rate, data = sqldf("select rate, latency from o where percentile= 'P75'")))
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o$percentile <- revalue(o$percentile, c("MAX"="100%", "MIN" = "0%", "P99.9" = "99.9%", "P99" = "99%", "P75" = "75%", "P50" = "50%", "P25" = "25%"))
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o$percentile <-factor(o$percentile,levels=c("100%", "99.9%", "99%", "75%", "50%", "25%", "1%", "0.1%", "0%"))
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o$rate <- factor(o$rate)
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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)) +
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geom_bar(stat='identity', position='identity', width=1) +
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#scale_fill_grey() +
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scale_fill_viridis_d(expand = c(0, 0)) +
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scale_y_continuous(expand = c(0, 0)) +
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scale_x_discrete(expand = c(0, 0)) +
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ylab("Latency (ms)") +
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xlab("Packets / second") +
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labs(fill="Distribution") +#, title="low values") +
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coord_cartesian(ylim=c(0,500)) +
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geom_hline(yintercept = 200) +
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geom_hline(yintercept = 400) +
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#geom_abline(intercept = q1coefs[1], slope = q1coefs[2], linetype='dashed') +
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#geom_abline(intercept = mcoefs[1], slope = mcoefs[2], linetype='dashed') +
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#geom_abline(intercept = q3coefs[1], slope = q3coefs[2], linetype='dashed') +
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theme_classic()
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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)) +
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geom_bar(stat='identity', position='identity') +
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scale_fill_grey() +
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scale_y_log10() +
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ylab("Latency (ms)") +
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xlab("Packets / second") +
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labs(fill="Percentile", title="high values (log scale)") +
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coord_cartesian(ylim=c(100,100000)) +
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geom_hline(yintercept = 200) +
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geom_hline(yintercept = 400) +
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theme_classic()
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plot_grid(gp, go, align = "v", axis = "l", ncol=1) + ggsave("torrate.pdf", dpi=300, dev='pdf', height=12, width=15, units="cm")
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go + ggsave("torrate_scale.pdf", dpi=300, dev='pdf', height=6, width=12, units='cm')
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