Code cleaning

r/config_light.R

@@ -1,414 +0,0 @@
-library(ggplot2)
-library(sqldf)
-library(plyr)
-library(dplyr)
-library(cowplot)
-
-scale_fill_bw_qdu_6 <- scale_fill_manual(values = c("#000000", "#333333", "#666666", "#999999", "#cccccc", "#ffffff"))
-scale_fill_bw_qdu_4 <- scale_fill_manual(values = c("#000000", "#555555", "#aaaaaa", "#ffffff"))
-
-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"="TickTock", "0"="Duplicate"))
-d <- d %>% mutate (lat = latency / 1000)
-dwin <- sqldf("select * from d where percentile != 'MAX' and percentile != 'P1' and percentile != 'P0.1'") %>% arrange(percentile)
-gd <- ggplot(dwin, aes(x=window,y=lat,group=percentile,fill=percentile)) +
-  geom_bar(stat='identity', position='identity', color="black", width=1) +
-  scale_fill_grey() +
-  ylab("Latency (ms)") +
-  xlab("Window (sec)") +
-  labs(fill="Percentile") +
-  scale_x_discrete(expand = c(0, 0)) + 
-  scale_y_continuous(expand = c(0, 0)) +
-  coord_cartesian(ylim=c(0,600)) +
-  geom_hline(yintercept = 160, linetype="dashed", color="white") +
-  annotate("text", x=4, y=160, label = "QoS: OWD best", vjust = -1, color="white") +
-  geom_hline(yintercept = 360) +
-  annotate("text", x=4, y=360, label = "QoS: OWD limit", vjust = -1,color="black") +
-  theme_classic() +
-  theme(legend.position="none") +
-  facet_grid( . ~ strat)
-
-
-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"="TickTock", "0"="Duplicate"))
-e$percentile <-factor(e$percentile,levels=c("MAX", "P99.9", "P99", "P75", "P50", "P25", "P1", "P0.1", "MIN"))
-e <- e %>% mutate (lat = latency / 1000)
-elinks <- sqldf("select * from e where percentile != 'MAX'  and percentile != 'P1' and percentile != 'P0.1' and links != 6 and links != 4") %>% arrange(percentile)
-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', width=1, color="black") +
-  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"="TickTock", "0"="Duplicate"))
-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)
-ffast <- sqldf("select * from f where percentile != 'MAX'  and percentile != 'P1' and percentile != 'P0.1'") %>% arrange(percentile)
-gf <- ggplot(ffast, 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, gdbis, ge, ge, gf, gf, ncol=3) + 
-  ggsave("light_config.pdf", dpi=300, dev='pdf', height=17, width=15, units="cm")
-
-mega <- sqldf("
-select window as variable, lat, strat, percentile, 'Window (sec)' as variable_name from dwin
-union
-select links as variable, lat, strat, percentile, '# of total links' as variable_name from elinks
-union
-select fast_count as variable, lat, strat, percentile, '# of fast links' as variable_name from ffast
-") %>% arrange(percentile)
-
-mega$percentile <- revalue(mega$percentile, c("P99.9" = "99.9%", "P99" = "99%", "P75"="75%", "P50" = "50%", "P25" = "25%", "MIN" = "Min"))
-mega$variable <- factor(mega$variable, levels =c("0.2", "1", "2", "3", "4", "5", "6", "8", "10", "12", "13", "14", "16", "20", "34"))
-
-QoS <- data.frame(x = c(1.2, 1.5), name = c("bar", "foo"))                       
-
-ggplot(mega, aes(x=variable,y=lat,group=percentile,fill=percentile)) +
-  geom_bar(stat='identity', position='identity', color="black", width=1) +
-  scale_fill_bw_qdu_6 +
-  ylab("Latency (ms)") +
-  xlab("Protocol Parameter Value") +
-  labs(fill="Distribution") +
-  scale_x_discrete(expand = c(0, 0)) + 
-  scale_y_continuous(expand = c(0, 0)) +
-  coord_cartesian(ylim=c(0,600)) +
-  geom_hline(aes(yintercept = 110, linetype="Target One Way Delay"), color="grey") +
-  #annotate("text", x=4, y=160, label = "OWD best", vjust = 1.5, color="white") +
-  geom_hline(aes(yintercept = 360, linetype="Max One Way Delay"), color="grey") +
-  #annotate("text", x=4, y=360, label = "OWD limit", vjust = -1, color="black") +
-  theme_classic() +
-  theme(legend.position="top", 
-    legend.direction = "horizontal", 
-    legend.box = "vertical", 
-    panel.spacing.y = unit(4, "mm"), 
-    strip.placement = "outside",
-    legend.margin=margin(t = -0.1, unit='cm')) +
-  guides(fill = guide_legend(nrow = 1)) +
-  facet_grid(strat ~ variable_name, scales = "free",  switch = "x") + 
-  scale_linetype_manual(name = "QoS", values = c(1, 2), guide = guide_legend(override.aes = list(linetype = c("solid", "dashed")))) +
-  ggsave("light_config.pdf", dpi=300, dev='pdf', height=12.5, width=12.5, units="cm")
-
-
-
-g <- read.csv("../../donar-res/tmp_light/guards.csv")
-g$strat <- as.factor(g$strat)
-g$strat <- revalue(g$strat, c("1"="TickTock", "0"="Duplicate"))
-g$guard <- as.factor(g$guard)
-g$percentile <- revalue(g$percentile, c("MAX" = "100%", "P99.9"="99.9%", "P99"="99%", "P75" = "75%", "P50"="50%","P25"="25%","P1"="1%","P0.1"="0.1%","MIN"="Min"))
-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("100%", "99.9%", "99%", "75%", "50%", "25%", "1%", "0.1%", "Min"))
-g <- g %>% mutate (lat = latency / 1000)
-gg <- ggplot(sqldf("select * from g where percentile != '100%' and percentile != '1%' and percentile != '0.1%' ") %>% arrange(percentile), aes(x=guard,y=lat,group=percentile,fill=percentile)) +
-  geom_bar(stat='identity', position='identity', width=1, color="black") +
-  scale_fill_bw_qdu_6 +
-  scale_x_discrete(expand = c(0, 0)) + 
-  scale_y_continuous(expand = c(0, 0)) +
-  coord_cartesian(ylim=c(0,600)) +
-  ylab("Latency (ms)") +
-  xlab("# of guards") +
-  labs(fill="Distribution") +
-  scale_linetype_manual(name = "QoS", values = c(1, 2), guide = guide_legend(override.aes = list(linetype = c("solid", "dashed")))) +
-  geom_hline(aes(yintercept = 110, linetype="Target One Way Delay"), color="grey") +
-  geom_hline(aes(yintercept = 360, linetype="Max One Way Delay"), color="grey") +
-  facet_grid(. ~ strat, scales = "free",  switch = "x") + 
-  theme_classic() +
-  guides(fill = guide_legend(nrow = 1)) +
-  theme(strip.placement = "outside",
-        legend.position="top", 
-        legend.direction = "horizontal", 
-        legend.box = "vertical",
-        legend.margin=margin(t = -0.2, l=-0.8, unit='cm'))
-
-gg + ggsave("light_guards.pdf", dpi=300, dev='pdf', height=7.5, width=12.5, units="cm")
-
-h <- read.csv("../../donar-res/tmp_light/complem.csv")
-h$strat <- revalue(h$strat, c("ticktock"="TickTock", "duplicate"="Duplicate"))
-h$percentile <- revalue(h$percentile, c("MAX" = "100%", "P99.9"="99.9%", "P99"="99%", "P75" = "75%", "P50"="50%","P25"="25%","P1"="1%","P0.1"="0.1%","MIN"="Min"))
-h$percentile <-factor(h$percentile,levels=c("100%", "99.9%", "99%", "75%", "50%", "25%", "1%", "0.1%", "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 != '100%' and percentile != '1%' and percentile != '0.1%' ") %>% arrange(percentile), aes(x=mode,y=lat,group=percentile,fill=percentile)) +
-  geom_bar(stat='identity', position='identity', width=1, color="black") +
-  scale_fill_bw_qdu_6 +
-  scale_x_discrete(expand = c(0, 0)) + 
-  scale_y_continuous(expand = c(0, 0)) +
-  scale_linetype_manual(name = "QoS", values = c(1, 2), guide = guide_legend(override.aes = list(linetype = c("solid", "dashed")))) +
-  geom_hline(aes(yintercept = 110, linetype="Target One Way Delay"), color="grey") +
-  geom_hline(aes(yintercept = 360, linetype="Max One Way Delay"), color="grey") +
-  coord_cartesian(ylim=c(0,500)) +
-  ylab("Latency (ms)") +
-  xlab("Feature") +
-  labs(fill="Distribution") +
-  facet_grid(. ~ strat, scales = "free",  switch = "x") + 
-  theme_classic() +
-  guides(fill = guide_legend(nrow = 1)) +
-  theme(strip.placement = "outside",
-        legend.position="top", 
-        legend.direction = "horizontal", 
-        legend.box = "vertical",
-        legend.margin=margin(t = -0.2, l=-0.8, unit='cm'))
-
-gh + ggsave("light_complementary.pdf", dpi=300, dev='pdf', height=7.5, width=12.5, 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"="Min"))
-i$percentile <-factor(i$percentile,levels=c("100%", "99.9%", "99%", "75%", "50%", "25%", "1%", "0.1%", "Min"))
-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%' ") %>% arrange(percentile), aes(x=algo,y=lat,group=percentile,fill=percentile)) +
-  geom_bar(stat='identity', position='identity',width=1,color="black") +
-  scale_fill_bw_qdu_6 +
-  scale_y_continuous(expand = c(0, 0)) +
-  scale_x_discrete(expand = c(0, 0)) +
-  #scale_fill_viridis_d() +
-  geom_hline(aes(yintercept = 110, linetype="Target One Way Delay"), color="grey") +
-  geom_hline(aes(yintercept = 360, linetype="Max One Way Delay"), color="grey") +
-  coord_cartesian(ylim=c(0,600)) +
-  ylab("Latency (ms)") +
-  xlab("Algorithm") +
-  labs(fill="Distribution", title="linear scale, cropped") +
-  theme_classic() +
-  scale_linetype_manual(name = "QoS", values = c(1, 2), guide = guide_legend(override.aes = list(linetype = c("solid", "dashed")))) +
-  guides(fill = guide_legend(nrow = 1)) +
-  facet_grid(. ~ secmode, scales = "free",  switch = "x") + 
-  theme(axis.text.x = element_text(angle = 20, hjust=1), 
-        plot.margin = unit(c(0.3,0.2,0.2,1), "cm"),
-        strip.placement = "outside",
-        legend.position="none", 
-        legend.direction = "horizontal", 
-        legend.box = "vertical",
-        legend.margin=margin(t = -0.1, unit='cm'))
-
-gibis <- ggplot(sqldf("select * from i where percentile != '100%' and percentile != '1%' and percentile != '0.1%' ") %>% arrange(percentile), aes(x=algo,y=lat,group=percentile,fill=percentile)) +
-  geom_bar(stat='identity', position='identity', color="black", width=1) +
-  scale_fill_bw_qdu_6 +
-  scale_y_log10() +
-  #geom_hline(aes(yintercept = 160, linetype="Target One Way Delay"), color="grey") +
-  #geom_hline(aes(yintercept = 360, linetype="Max One Way Delay"), color="grey") +
-  scale_x_discrete(expand = c(0, 0)) +
-  coord_cartesian(ylim=c(30,250000)) +
-  ylab("Latency (ms)") +
-  xlab("Algorithm") +
-  labs(fill="Distribution", title="log scale, full") +
-  facet_grid(. ~ secmode, scales = "free",  switch = "x") + 
-  theme_classic() +
-  theme(legend.position="none",
-        axis.text.x = element_text(angle = 20, hjust=1), 
-        plot.margin = unit(c(0.2,0.2,0.2,1), "cm"),
-        strip.placement = "outside")
-
-gilegend <- get_legend(gi + theme(legend.position = "top"))
-
-plot_grid(gilegend, plot_grid(gibis, gi, align="v", ncol=1), ncol=1, rel_heights = c(1,6)) + 
-  ggsave("light_battle.pdf", dpi=300, dev='pdf', height=15, width=12.5, 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")
-
-fastprobe2 = read.csv(text="
-strat,schedule_state,packet_position,ratio
-Ticktock,tick,original on 1st class,0.946975638
-Ticktock,tick,piggybacked on 2nd class,0.053025421
-Ticktock,tock,original on 2nd class,0.474113253
-Ticktock,tock,piggybacked on 1st class,0.525885688
-Duplicate,all,original on 1st class,0.7349205324574037
-Duplicate,all,piggybacked on 1st class,0.13431755481879454
-Duplicate,all,original on 2nd class,0.1154165656941915
-Duplicate,all,piggybacked on 2nd class,0.015345347029610245
-")
-fastprobe2$schedule_state <-factor(fastprobe2$schedule_state,levels=c("all", "tock", "tick"))
-ggplot(fastprobe2, aes(x=schedule_state, y=ratio, fill=packet_position)) +
-  geom_bar(stat='identity', position='stack', width=1, color="black") +
-  facet_grid(strat ~ ., scales = "free",  switch = "x") + 
-  scale_y_continuous(expand = c(0, 0), labels = scales::percent) +
-  scale_x_discrete(expand = c(0, 0)) +
-  scale_fill_bw_qdu_4 +
-  xlab("Schedule State") +
-  ylab("Fastest Delivery") +
-  labs(fill="Scheduling Decision") +
-  coord_flip() +
-  theme_classic() +
-  ggsave("light_fastprobe.pdf", dpi=300, dev='pdf', height=5, width=12.5, 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', color="black") +
-  scale_fill_grey() +
-  ylab("Calls") +
-  xlab("Links fast at least once") +
-  labs(fill="Strategy") +
-  scale_y_continuous(expand = c(0, 0), labels = scales::percent) +
-  scale_x_continuous(expand = c(0, 0)) +
-  theme_classic()
-  #theme(axis.text.x = element_text(angle = 20, hjust=1))
-gk + ggsave("light_fastlinks.pdf", dpi=300, dev='pdf', height=6, width=12.5, 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)) +
-  scale_y_continuous(expand = c(0, 0)) +
-  scale_x_continuous(expand = c(0, 0)) +
-  xlab("Packet Identifier") +
-  ylab("Latency (ms)") +
-  labs(linetype="Way") +
-  geom_point(size=0.1) +
-  geom_hline(aes(yintercept = 110, linetype="Target One Way Delay"), color="white") +
-  geom_hline(aes(yintercept = 360, linetype="Max One Way Delay"), color="grey") +
-  scale_linetype_manual(name = "QoS", values = c(1, 2), guide = guide_legend(override.aes = list(linetype = c("solid", "dashed")))) +
-  theme_classic() +
-  theme(legend.position="top",legend.margin=margin(t = -0.1, b = -0.1, unit='cm'))
-#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)
-m$group <- revalue(m$group, c("fast" = "1st class links", "probe" = "2nd class links"))
-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)) +
-  scale_y_continuous(expand = c(0, 0)) +
-  scale_x_continuous(expand = c(0, 0)) +
-  xlab("Packet Identifier") +
-  ylab("Latency (ms)") +
-  labs(color="Link Group") +
-  labs(linetype="Way") +
-  geom_point(size=0.1) +
-  geom_hline(yintercept = 110, linetype="dashed", color="white") +
-  geom_hline(yintercept = 360, linetype="solid", color="grey") +
-  guides(color = guide_legend(override.aes = list(size=2))) +
-  theme_classic() +
-  theme(legend.position="top", legend.margin=margin(t = -0.1, b = -0.1, unit='cm'))
-
-n <- read.csv("../../donar-res/tmp_light/linkgroup.csv")
-n$group <- revalue(n$group, c("fast" = "1st class links", "probe" = "2nd class links"))
-#n$link_id <- as.factor(n$link_id)
-gn <- ggplot(n, aes(x=packet_id, y=link_id, color=group)) +
-  scale_color_grey() +
-  scale_fill_grey() +
-  geom_point(size=0.6) +
-  scale_y_continuous(expand = c(0.1, 0.1)) +
-  scale_x_continuous(expand = c(0, 0)) +
-  #scale_color_viridis_d() +
-  xlab("Packet Identifier") +
-  ylab("Link Identifier") +
-  labs(color="Link group") +
-  #geom_tile() +
-  theme_classic() +
-  theme(legend.position="none")
-
-plot_grid(gl, gm, gn, ncol=1,align="v",rel_heights = c(1.3,1.3,1)) + ggsave("light_single.pdf", dpi=300, dev='pdf', height=12.5, width=12.5, 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')

r/lightning_begin.R

@@ -1,172 +0,0 @@
-library(ggplot2)
-library(sqldf)
-library(plyr)
-library(dplyr)
-library(cowplot)
-
-link_info <- read.csv("../res/tmp_graph/u.txt")
-ggplot(data=link_info, aes(x=timestamp, y=link, color=speed)) +
-  #geom_line() +
-  geom_point() +
-  theme_classic()
-
-
-xx <- read.csv("../../donar-res/tmp_light/v2.csv")
-xx2 <- sqldf("select packet_id,1.0 * MIN(latency) / 1000.0 as lat,way from xx group by packet_id,way")
-ggplot(data=xx2, aes(x=packet_id, y=lat, color=way)) +
-  geom_line() +
-  geom_hline(yintercept=400) +
-  geom_hline(yintercept=200) +
-  coord_cartesian(ylim=c(0,1000)) +
-  #geom_point() +
-  theme_classic()
-
-xx4 <- sqldf("select packet_id,1.0 * MIN(latency) / 1000.0 as lat,way from xx where flag = 0 group by packet_id,way")
-ggplot(data=xx4, aes(x=packet_id, y=lat, color=way)) +
-  geom_line() +
-  geom_hline(yintercept=400) +
-  geom_hline(yintercept=200) +
-  coord_cartesian(ylim=c(0,1000)) +
-  #geom_point() +
-  theme_classic()
-
-
-xx5 <- sqldf("select packet_id,1.0 * MIN(latency) / 1000.0 as lat,way from xx where flag = 1 group by packet_id,way")
-ggplot(data=xx5, aes(x=packet_id, y=lat, color=way)) +
-  geom_line() +
-  geom_hline(yintercept=400) +
-  geom_hline(yintercept=200) +
-  coord_cartesian(ylim=c(0,1000)) +
-  #geom_point() +
-  theme_classic()
-
-
-prepros <- sqldf(
-"select 
-  r.packet_id, 
-  r.way, 
-  r.lat, 
-  s.flag 
-from 
-  (select 
-     packet_id,
-     way,
-     min(latency) as lat 
-   from xx 
-  group by packet_id,way) as r, 
-  xx as s 
-where 
-  s.packet_id = r.packet_id and 
-  s.way = r.way and 
-  r.lat = s.latency")
-
-xx3 <- sqldf("select packet_id,1.0 * MIN(latency) / 1000.0 as lat,flag,way from xx group by packet_id,way,flag")
-xx3$flag <- factor(xx3$flag)
-ggplot(data=xx3, aes(x=lat, group=flag, color=flag)) +
-  stat_ecdf(pad = FALSE) +
-  geom_vline(xintercept = 200) +
-  geom_vline(xintercept = 400) +
-  coord_cartesian(xlim=c(0,1200)) +
-  theme_classic()
-
-xy <- read.csv("../../donar-res/tmp_light/light.csv")
-xz <- sqldf("select packet_id,1.0 * MIN(latency) / 1000.0 as lat,way,conf,run from xy where packet_id > 50 and packet_id < 7400 group by packet_id,way,conf,run")
-xz$conf <- factor(xz$conf)
-ggplot(data=xz, aes(x=lat, group=conf, color=conf)) +
-  stat_ecdf(pad = FALSE) +
-  geom_vline(xintercept = 200) +
-  geom_vline(xintercept = 400) +
-  coord_cartesian(xlim=c(0,600)) +
-  theme_classic()
-
-ggplot(data=xz, aes(y=lat, x=conf)) +
-  geom_violin(scale='width') +
-  geom_boxplot(width=0.1, outlier.shape=NA) +
-  theme_classic()
-
-xa <- sqldf("select packet_id,1.0 * MIN(latency) / 1000.0 as lat,way,conf,run from xy group by packet_id,way,conf,run")
-ggplot(data=sqldf("select * from xa where run='out/I0mj7t5OJu9DGMq1-6'"), aes(x=packet_id, y=lat, color=way)) +
-  geom_line() +
-  geom_hline(yintercept=400) +
-  geom_hline(yintercept=200) +
-  coord_cartesian(ylim=c(0,1000)) +
-  #geom_point(aes(shape=conf)) +
-  theme_classic()
-
-xb <- read.csv("../../donar-res/tmp_light/v1.csv")
-xb$flag <- factor(xb$flag)
-xb$link_id <- factor(xb$link_id)
-xc <- sqldf("select *, 1.0 * latency / 1000.0 as lat from xb where vanilla = 1 and link_id = 5")
-ggplot(data=xc, aes(x=packet_id, y=lat, color=link_id:way)) +
-  coord_cartesian(ylim=c(100,600)) +
-  geom_line() +
-  #geom_point() +
-  theme_classic()
-
-ggplot(data=sqldf("select 
-  packet_id,way,latency,1.0 * MIN(latency) / 1000 as lat 
-from xb 
-group by packet_id,way"), aes(x=packet_id, y=lat, color=way)) +
-  coord_cartesian(ylim=c(100,600)) +
-  geom_line() +
-  #geom_point() +
-  theme_classic()
-
-
-xd <- sqldf("
-select
-  lat,
-  xb.latency,
-  vanilla,
-  xb.packet_id,
-  xb.way,
-  link_id,
-  flag
-from
-(select 
-  packet_id,way,latency,1.0 * MIN(latency) / 1000 as lat 
-from xb 
-group by packet_id,way) nn,
-xb
-where
-  xb.latency = nn.latency and
-  xb.packet_id = nn.packet_id and
-  xb.way = nn.way
-")
-ggplot(data=xd, aes(x=packet_id, y=lat, color=link_id)) +
-  #coord_cartesian(ylim=c(0,1000),xlim=c(3200,3500)) +
-  geom_line() +
-  theme_classic()
-
-ggplot(data=sqldf("select * from xb where vanilla = 1 and way= 'client'"), aes(x=packet_id, y=link_id, color=flag)) +
-  geom_point() +
-  theme_classic()
-
-torbw <- read.csv("../../donar-res/tmp_light/tor_bw.csv")
-torbw <- sqldf("select *, 1.0 * latency / 1000.0 as lat from torbw")
-
-torbw2 <- torbw %>%
-  dplyr::group_by(rate) %>%
-  dplyr::summarise(
-    third = quantile(lat,0.75),
-    median = median(lat)
-  )
-
-coefs <- coef(lm(median ~ rate, data = torbw2))
-coefs2 <- coef(lm(third ~ rate, data = torbw2))
-
-
-lats <- ggplot(torbw, aes(x=rate,y=lat, group=rate)) +
-  coord_cartesian(ylim=c(0,650)) +
-  geom_hline(yintercept=400, linetype="dashed") +
-  geom_hline(yintercept=200, linetype="dashed") +
-  ylab("latency (ms)") +
-  xlab("packets/sec") +
-  geom_abline(intercept = coefs[1], slope = coefs[2]) +
-  geom_abline(intercept = coefs2[1], slope = coefs2[2]) +
-  geom_boxplot(outlier.shape = NA) +
-  theme_classic()
-
-lats + ggsave("tor_bw.png", dpi=300, dev='png', height=8, width=15, units="cm")
-
-lightlinks <- read.csv("../../donar-res/tmp_light/lightning-links.csv")

r/thunder_configure.R

@@ -1,431 +0,0 @@
-library(ggplot2)
-library(sqldf)
-library(plyr)
-library(cowplot)
-
-thunder_ms <- read.csv("thunder_22.csv")
-thunder_ms <- sqldf("select run,ident,jmax,links,latency, CAST(latency as real) / 1000. as lat_ms from thunder_ms")
-thunder_ms$links <- as.factor(thunder_ms$links)
-thunder_ms$jmax <- as.factor(thunder_ms$jmax)
-
-ggplot(data = thunder_ms, aes(x = links, y=lat_ms)) +
-  #geom_violin() +
-  geom_boxplot(outlier.size=0.1) +
-  geom_hline(yintercept=800, color = "red", linetype="dashed") +
-  geom_hline(yintercept=400, color = "DarkGreen") +
-  annotate("text", x=7.1, y=650, label= "high", color="DarkGreen") +
-  annotate("text", x=7, y=1200, label= "acceptable", color="red") +
-  coord_cartesian(xlim = c(1, 7), ylim = c(0, 1000)) +
-  #ylim(0,1000) +
-  scale_fill_grey() +
-  #scale_y_log10() +
-  ylab("latency (ms)") +
-  xlab("links") +
-  theme_classic()
-
-v1 <- ggplot(data = thunder_ms, aes(x = jmax, y=lat_ms, fill=links)) +
-  #geom_violin() +
-  geom_boxplot(outlier.size=0.1) +
-  geom_hline(yintercept=800, color = "red", linetype="dashed") +
-  geom_hline(yintercept=400, color = "DarkGreen") +
-  annotate("text", x=7.1, y=650, label= "high", color="DarkGreen") +
-  annotate("text", x=7, y=1200, label= "acceptable", color="red") +
-  coord_cartesian(xlim = c(1, 7), ylim = c(0, 1000)) +
-  #ylim(0,1000) +
-  scale_fill_grey() +
-  #scale_y_log10() +
-  ylab("latency (ms)") +
-  xlab("max allowed jitter") +
-  theme_classic()
-
-#v1 + ggsave("thunder_configure_ms.png", dpi=300, dev='png', height=5, width=15, units="cm")
-
-thunder_rcv <- sqldf("select run,jmax,links,(9900-COUNT(latency)*1.0)/9900 as dlv from thunder_ms group by jmax,links,run")
-thunder_rcv$jmax <- as.factor(thunder_rcv$jmax)
-thunder_rcv$links <- as.factor(thunder_rcv$links)
-v2 <- ggplot(data = thunder_rcv, aes(x = jmax, y=dlv, fill=links)) +
-  geom_boxplot(outlier.size=0.1) +
-  #geom_violin(scale='width') +
-  scale_y_continuous(labels = scales::percent) +
-  scale_fill_grey() +
-  #scale_y_log10(labels = scales::percent) +
-  ylab("dropped packets") +
-  xlab("max allowed jitter") +
-  theme_classic()
-
-thunder_bw <- read.csv("thunder_22_bw.csv")
-thunder_bw <- sqldf("select run,jmax,links,udp_sent,udp_rcv,cells_sent,cells_rcv,1.0*cells_sent/udp_sent as sent_ratio,1.0*cells_rcv/udp_rcv as rcv_ratio from thunder_bw where udp_sent > 4000")
-thunder_bw$jmax <- as.factor(thunder_bw$jmax)
-thunder_bw$links <- as.factor(thunder_bw$links)
-
-v3 <- ggplot(data = thunder_bw, aes(x = jmax, y=sent_ratio, fill=links)) +
-  geom_boxplot(outlier.size=0.1) +
-  #scale_y_log10() +
-  coord_cartesian(ylim = c(1,4)) +
-  scale_fill_grey() +
-  ylab("bandwidth ratio") +
-  xlab("max allowed jitter") +
-  theme_classic()
-
-
-t1 <- plot_grid(v1, v2, v3, labels = c('A', 'B', 'C'), ncol=1)
-t1 + ggsave("thunder_configure.png", dpi=300, dev='png', height=15, width=15, units="cm")
-
-thunder_links <- read.csv("thunder_configure_20_links.csv")
-links_down_at_least_once <- sqldf("select run,xp_time,link_id, COUNT(status) as downcount, SUM(delta) as elapsed from thunder_links where status='down' group by run,link_id,xp_time")
-links_down_at_least_once2 <- sqldf("select row_number () OVER (PARTITION BY run ORDER BY elapsed DESC) sorting,run,link_id,downcount,elapsed,xp_time,1.0*elapsed/xp_time down_ratio from links_down_at_least_once")
-links_down_at_least_once2$sorting <- as.factor(links_down_at_least_once2$sorting)
-v4 <- ggplot(data = links_down_at_least_once2, aes(x = sorting, y=down_ratio)) +
-  #geom_violin() +
-  #geom_boxplot(width=0.2) +
-  #scale_y_log10() +
-  #scale_y_log10(labels = scales::percent) +
-  geom_bar(stat="summary") +
-  #scale_y_log10() +
-  scale_y_continuous(labels = scales::percent) +
-  scale_fill_grey() +
-  ylab("Cumulated downtime") +
-  xlab("Sorted links") +
-  theme_classic()
-
-downtime <- sqldf(
-"
-select sorting,1.0*duration/1000 as dur 
-from thunder_links as tl
-inner join links_down_at_least_once2 as l2 on 
-  tl.run = l2.run
-  and tl.link_id = l2.link_id 
-where will_change='True' 
-  and status='down'")
-
-v5 <- ggplot(data = downtime, aes(x=sorting, y=dur)) +
-  geom_violin() +
-  geom_boxplot(width=0.1, outlier.shape = NA) +
-  scale_y_log10() +
-  ylab("Downtime (in sec)") +
-  xlab("Sorted links") +
-  theme_classic()
-
-gobal_links <- sqldf(
-"
-select ts,run,durations_global,12-COUNT(status) as down_link_count,xp_time
-from thunder_links 
-where will_change_global='True' and status='up' and durations_global > 0
-group by ts,run,durations_global
-")
-
-down_group_ratio <- sqldf(
-"
-select run,1.0 * SUM(durations_global)/xp_time as down_ratio, down_link_count
-from gobal_links
-where down_link_count >= 0
-group by run,down_link_count,xp_time
-")
-
-down_group_ratio$down_link_count <- as.factor(down_group_ratio$down_link_count)
-v6 <- ggplot(data = down_group_ratio, aes(x=down_link_count, y=down_ratio)) +
-  geom_bar(stat="summary") +
-  #scale_y_log10() +
-  scale_y_continuous(labels = scales::percent) +
-  ylab("Cumulated downtime") +
-  xlab("Number of links down at once") +
-  theme_classic()
-
-downtime_group <- sqldf(
-"
-select down_link_count, 1.0*durations_global/1000 as dur from gobal_links  where down_link_count >= 0
-")
-downtime_group$down_link_count <- as.factor(downtime_group$down_link_count)
-v7 <- ggplot(data = downtime_group, aes(x=down_link_count, y=dur)) +
-  geom_violin() +
-  geom_boxplot(width=0.1, outlier.shape = NA) +
-  scale_y_log10() +
-  ylab("Downtime (in sec)") +
-  xlab("Number of links down at once") +
-  theme_classic()
-
-t2 <- plot_grid(v4, v5, v6, v7, labels = c('A', 'B', 'C', 'D'), ncol=2)
-t2 + ggsave("thunder_links.png", dpi=300, dev='png', height=12, width=15, units="cm")
-
-latency_evol <- sqldf(
-"
-select sorting,lat_ms,ident,tm.jmax,tm.links
-from 
-  thunder_ms as tm,
-  (select run,jmax,links,row_number () OVER (ORDER BY links DESC) sorting from thunder_ms group by run,jmax,links ORDER BY links DESC  limit 0,1) as sel_run
-where
-  tm.run = sel_run.run and tm.jmax = sel_run.jmax and tm.links = sel_run.links
-")
-
-latency_evol$sorting <- as.factor(latency_evol$sorting)
-v8 <- ggplot(data=latency_evol, aes(x=ident,y=lat_ms)) +
-  geom_line() +
-  xlab("Packet identifier") +
-  ylab("Latency (ms)") +
-  theme_classic()
-
-thunder_drop <- read.csv("thunder_configure_partial_drop.csv")
-thunder_drop_2 <- sqldf("select run, packet_range, 1.0*count / 990 as packet_ratio, row_number() OVER (partition by packet_range order by run) sorting from thunder_drop where run LIKE '%-23' ")
-#cats <- c("0-989","990-1979","1980-2969","2970-3959","3960-4949","4950-5939","5940-6929","6930-7919","7920-8909","8910-9899")
-thunder_drop_2$packet_range <- as.factor(thunder_drop_2$packet_range)
-thunder_drop_2$sorting <- as.factor(thunder_drop_2$sorting)
-
-#thunder_drop_2$packet_range <- factor(
-#  mapvalues(thunder_drop_2$packet_range, cats, cats),
-#  levels = cats,
-#  ordered = TRUE)
-
-v9 <- ggplot(data = thunder_drop_2, aes(x=packet_range, y=packet_ratio,fill=sorting)) +
-  geom_bar(stat="summary",position = "dodge") +
-  #grom_bar() +
-  #scale_y_log10() +
-  scale_y_continuous(labels = scales::percent) +
-  ylab("Packets dropped") +
-  xlab("Packet identifier") +
-  labs(fill="Run") +
-  scale_fill_grey() +
-  theme_classic() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.key.size = unit(0.2, "cm")) 
-
-thunder_drop_burst <- read.csv("thunder_configure_partial_drop_burst.csv")
-tdb_ag <- sqldf("select run,count,COUNT(count) as oc from thunder_drop_burst where run LIKE '%-23' group by run,count")
-tdb_ag_2 <- sqldf(
-"
-select 
-  td.run as r,
-  count,
-  oc,
-  total,
-  1.0 * oc / total as oc_ratio,
-  row_number() OVER (partition by count order by td.run) as sorting 
-from 
-  tdb_ag as td,
-  (select run,SUM(oc) as total from tdb_ag group by run) as ag
-where
-  td.run = ag.run
-")
-
-tdb_ag_2$sorting <- as.factor(tdb_ag_2$sorting)
-tdb_ag_2$count <- as.factor(tdb_ag_2$count)
-
-v10 <- ggplot(data = tdb_ag_2, aes(x=count, y=oc_ratio)) +
-  #geom_bar(stat="summary",position = "dodge") +
-  #scale_y_log10() +
-  geom_violin(scale='width') +
-  geom_boxplot(width=0.1, outlier.shape=NA) +
-  scale_y_continuous(labels = scales::percent) +
-  ylab("% observed drops") +
-  xlab("Packets lost during the drop") +
-  scale_fill_grey() +
-  theme_classic()
-
-thunder_red <- read.csv("thunder_configure_partial_red.csv")
-tred <- sqldf(
-"
-select 
-  tr.run as r, 
-  delivered_at_once,
-  1.0 * occur / total as occur_ratio,
-  occur,
-  total,
-  row_number() OVER (partition by delivered_at_once order by tr.run) as sorting 
-from 
-  thunder_red tr,
-  (select run,SUM(occur) as total from thunder_red group by run) as ag
-WHERE 
-  tr.run LIKE '%-23'
-  and tr.run = ag.run
-
-")
-tred$sorting <- as.factor(tred$sorting)
-tred$delivered_at_once <- as.factor(tred$delivered_at_once)
-v11 <- ggplot(data = tred, aes(x=delivered_at_once, y=occur_ratio)) +
-  #geom_bar(stat="summary",position = "dodge") +
-  geom_violin(scale='width') +
-  xlab('Fresh packets per cell') +
-  ylab('% of received cells') +
-  scale_y_continuous(labels = scales::percent) +
-  geom_boxplot(width=0.1, outlier.shape=NA) +
-  theme_classic()
-
-t3 <- plot_grid(v8, v9, v10, v11, labels = c('A', 'B', 'C', 'D'), ncol=2)
-t3 + ggsave("thunder_packets.png", dpi=300, dev='png', height=12, width=15, units="cm")
-
-tor_multi_lat <- read.csv("tor_just_many_latencies.csv")
-tor_multi_lat_100 <- read.csv("tor_just_many_latencies_100.csv")
-
-tor_drop <- sqldf(
-"
-select run,conf,1.0*MAX(ident)/33 as last_one,'33' as pkt_sec from tor_multi_lat group by run,conf
-union
-select run,conf,1.0*MAX(ident)/10 as last_one, '10' as pkt_sec from tor_multi_lat_100 group by run,conf
-")
-
-v12 <- ggplot(data=tor_drop,aes(x=last_one, linetype=factor(pkt_sec, levels=c('33', '10')))) +
-  stat_ecdf(pad = FALSE) +
-  ylab("% broken links") +
-  labs(linetype="Pkt/sec") +
-  coord_cartesian(xlim = c(0, 300), ylim = c(0,0.5)) +
-  scale_y_continuous(labels = scales::percent) +
-  xlab("Elapsed time (sec)") + 
-  theme_classic()
-
-v12 + ggsave("broken.png", dpi=300, dev='png', height=5, width=15, units="cm")
-
-library(dplyr)
-library(purrr)
-library(tidyr) 
-
-tor_lat_stack <- tor_multi_lat %>%
-    dplyr::mutate(latency = latency / 1000) %>%
-    dplyr::group_by(run,conf) %>%
-    dplyr::summarise(
-      id = paste(first(run),first(conf)),
-      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, '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('P100','P99','P95','P75', 'P50', 'P25', 'P0')))
-) +
-  coord_cartesian(ylim = c(0,1500)) +
-  labs(fill="Percentile")+
-  xlab("Tor circuits") +
-  ylab("RTT (ms)") +
-  geom_bar(stat="identity", position="stack",width=1) +
-  scale_fill_grey() +
-  theme_classic() +
-  theme(axis.text.x=element_blank(), axis.ticks.x = element_blank(), legend.key.size = unit(0.2, "cm"))
-
-v14 <- ggplot(tor_lat_stack, aes(
-  x=reorder(id,median_sort),
-  y=quantile_value,
-  fill=factor(quantile_name, levels=c('P100','P99','P95','P75', 'P50', 'P25', 'P0')))
-) +
-  #coord_cartesian(ylim = c(0,1500)) +
-  labs(fill="Percentile")+
-  xlab("Tor circuits") +
-  ylab("RTT (ms)") +
-  geom_bar(stat="identity", position="stack",width=1) +
-  scale_fill_grey() +
-  theme_classic() +
-  theme(axis.text.x=element_blank(),axis.ticks.x = element_blank(),  legend.key.size = unit(0.2, "cm"))
-
-v15 <- ggplot(tor_lat_stack, aes(
-  x=reorder(id,max_sort),
-  y=quantile_value,
-  fill=factor(quantile_name, levels=c('P100','P99','P95','P75', 'P50', 'P25', 'P0')))
-) +
-  coord_cartesian(ylim = c(0,1500)) +
-  labs(fill="Percentile")+
-  xlab("Tor circuits") +
-  ylab("RTT (ms)") +
-  geom_bar(stat="identity", position="stack",width=1) +
-  scale_fill_grey() +
-  theme_classic() +
-  theme(axis.text.x=element_blank(), axis.ticks.x = element_blank(), legend.key.size = unit(0.2, "cm"), plot.tag.position='top')
-
-v16 <- ggplot(tor_lat_stack, aes(
-  x=reorder(id,max_sort),
-  y=quantile_value,
-  fill=factor(quantile_name, levels=c('P100','P99','P95','P75', 'P50', 'P25', 'P0')))
-) +
-  #coord_cartesian(ylim = c(0,1500)) +
-  labs(fill="Percentile")+
-  xlab("Tor circuits") +
-  ylab("RTT (ms)") +
-  geom_bar(stat="identity", position="stack",width=1) +
-  scale_fill_grey() +
-  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, 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 %>%
-  dplyr::mutate(latency = latency / 1000) %>%
-  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), 
-    max_sort = max(latency), 
-    median_sort = median(latency)
-  )
-
-tor_lat_stack_100 <- gather(tor_lat_stack_100, 'min', 'max', 'q25', 'median', 'q75', 'q95', 'q99', key="quantile_name", value="quantile_value")
-v17 <- ggplot(tor_lat_stack_100, aes(
-  x=reorder(id,median_sort),
-  y=quantile_value,
-  fill=factor(quantile_name, levels=c('max','q99','q95','q75', 'median', 'q25', 'min')))
-) +
-  coord_cartesian(ylim = c(0,1500)) +
-  labs(fill="quantile")+
-  xlab("Tor circuits") +
-  ylab("RTT (ms)") +
-  geom_bar(stat="identity", position="stack",width=1) +
-  scale_fill_grey() +
-  theme_classic() +
-  theme(axis.text.x=element_blank(), axis.ticks.x = element_blank(), legend.key.size = unit(0.2, "cm"))
-
-v18 <- ggplot(tor_lat_stack_100, aes(
-  x=reorder(id,median_sort),
-  y=quantile_value,
-  fill=factor(quantile_name, levels=c('max','q99','q95','q75', 'median', 'q25', 'min')))
-) +
-  #coord_cartesian(ylim = c(0,1500)) +
-  labs(fill="quantile")+
-  xlab("Tor circuits") +
-  ylab("RTT (ms)") +
-  geom_bar(stat="identity", position="stack",width=1) +
-  scale_fill_grey() +
-  theme_classic() +
-  theme(axis.text.x=element_blank(),axis.ticks.x = element_blank(),  legend.key.size = unit(0.2, "cm"))
-
-v19 <- ggplot(tor_lat_stack_100, aes(
-  x=reorder(id,max_sort),
-  y=quantile_value,
-  fill=factor(quantile_name, levels=c('max','q99','q95','q75', 'median', 'q25', 'min')))
-) +
-  coord_cartesian(ylim = c(0,1500)) +
-  labs(fill="quantile")+
-  xlab("Tor circuits") +
-  ylab("RTT (ms)") +
-  geom_bar(stat="identity", position="stack",width=1) +
-  scale_fill_grey() +
-  theme_classic() +
-  theme(axis.text.x=element_blank(), axis.ticks.x = element_blank(), legend.key.size = unit(0.2, "cm"), plot.tag.position='top')
-
-v20 <- ggplot(tor_lat_stack_100, aes(
-  x=reorder(id,max_sort),
-  y=quantile_value,
-  fill=factor(quantile_name, levels=c('max','q99','q95','q75', 'median', 'q25', 'min')))
-) +
-  #coord_cartesian(ylim = c(0,1500)) +
-  labs(fill="quantile")+
-  xlab("Tor circuits") +
-  ylab("RTT (ms)") +
-  geom_bar(stat="identity", position="stack",width=1) +
-  scale_fill_grey() +
-  theme_classic() +
-  theme(axis.text.x=element_blank(), axis.ticks.x = element_blank(), legend.key.size = unit(0.2, "cm"),plot.tag.position='bottom')
-
-t5 <- plot_grid(v20, v19, v18, v17, labels = c('A', 'B', 'C', 'D'), ncol=1)
-t5 + ggsave("tor_100ms.png", dpi=300, dev='png', height=20, width=15, units="cm")
-

r/thunder_configure_15_bw.csv

@@ -1,29 +0,0 @@
-run,jmax,links,udp_sent,udp_rcv,cells_sent,cells_rcv
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-NdTagQwY9eZOOUVL,250,2,5472,9938,10628,11511
-NdTagQwY9eZOOUVL,350,2,9899,10028,9923,9909
-NdTagQwY9eZOOUVL,450,2,9905,9912,9912,9908
-NdTagQwY9eZOOUVL,550,2,9898,9926,9911,9902
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-NdTagQwY9eZOOUVL,200,4,9728,9969,9924,15849
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-NdTagQwY9eZOOUVL,450,8,9893,9910,9911,9961
-NdTagQwY9eZOOUVL,550,8,8008,10031,9917,23757

r/thunder_configure_16_bw.csv

@@ -1,290 +0,0 @@
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-MdVXNUUYf2O820VO,250,4,9658,9967,10511,10431
-MdVXNUUYf2O820VO,350,4,9870,9911,9932,9918
-MdVXNUUYf2O820VO,450,4,9905,9968,9991,10002
-MdVXNUUYf2O820VO,550,4,9906,10027,9916,9915
-MdVXNUUYf2O820VO,50,6,5986,9969,16817,12607
-MdVXNUUYf2O820VO,150,6,8450,10025,10471,10644
-MdVXNUUYf2O820VO,200,6,9738,9908,10011,10021
-MdVXNUUYf2O820VO,250,6,9713,9909,10277,10388
-MdVXNUUYf2O820VO,350,6,8972,9979,10034,19342
-MdVXNUUYf2O820VO,450,6,9372,10216,10138,17938
-MdVXNUUYf2O820VO,550,6,9861,9966,9986,19301
-MdVXNUUYf2O820VO,50,8,5884,9910,22953,11090
-MdVXNUUYf2O820VO,150,8,8750,9908,11453,11263
-MdVXNUUYf2O820VO,200,8,9874,9908,10049,10679
-MdVXNUUYf2O820VO,250,8,9888,9910,10004,11330
-MdVXNUUYf2O820VO,350,8,9892,9908,9935,9935
-MdVXNUUYf2O820VO,450,8,9847,10035,9971,23757
-MdVXNUUYf2O820VO,550,8,9252,9912,9946,9845
-Nh4DvP2KogaVgfxv,250,8,9570,9908,10181,10205
-Nh4DvP2KogaVgfxv,350,8,9879,9969,10199,23712
-Nh4DvP2KogaVgfxv,450,8,9808,9908,9968,10061
-Nh4DvP2KogaVgfxv,550,8,9738,9909,9920,9871
-NqVYxVRzIMN5nlQR,200,8,8877,9907,10633,10426
-NqVYxVRzIMN5nlQR,250,8,9819,9908,10079,10268
-NqVYxVRzIMN5nlQR,350,8,9839,9908,9952,10171
-NqVYxVRzIMN5nlQR,450,8,9892,10206,10052,23738
-NqVYxVRzIMN5nlQR,550,8,9243,9907,9917,9853
-QK2TkEk5CeNvIzoo,50,2,7719,9910,10474,9589
-QK2TkEk5CeNvIzoo,150,2,9819,9966,10243,10204
-QK2TkEk5CeNvIzoo,200,2,9885,9914,9912,9909
-QK2TkEk5CeNvIzoo,250,2,9903,9967,9927,9919
-QK2TkEk5CeNvIzoo,350,2,9906,9909,9910,10412
-QK2TkEk5CeNvIzoo,450,2,9906,9912,9918,9921
-QK2TkEk5CeNvIzoo,550,2,9897,9940,9914,9907
-QK2TkEk5CeNvIzoo,50,4,4098,9909,15384,5428
-QK2TkEk5CeNvIzoo,150,4,9492,9907,10091,9893
-QK2TkEk5CeNvIzoo,200,4,9664,9910,9928,9890
-QK2TkEk5CeNvIzoo,250,4,9534,9912,10801,10628
-QK2TkEk5CeNvIzoo,350,4,9907,9909,9908,9908
-QK2TkEk5CeNvIzoo,450,4,9906,9909,9910,9908
-QK2TkEk5CeNvIzoo,550,4,9906,9913,9921,9919
-QK2TkEk5CeNvIzoo,50,6,5043,10024,12953,15817
-QK2TkEk5CeNvIzoo,150,6,7692,9923,10869,19529
-QK2TkEk5CeNvIzoo,200,6,8079,9912,10516,10060
-QK2TkEk5CeNvIzoo,250,6,9548,9911,10237,10334
-QK2TkEk5CeNvIzoo,350,6,9898,9968,9909,9910
-QK2TkEk5CeNvIzoo,450,6,9888,9922,10026,18265
-QK2TkEk5CeNvIzoo,550,6,9468,9908,9908,9845
-QK2TkEk5CeNvIzoo,50,8,4769,9907,21973,8120
-QK2TkEk5CeNvIzoo,150,8,9337,9909,10801,10884
-QK2TkEk5CeNvIzoo,200,8,9876,9907,10225,10475
-QK2TkEk5CeNvIzoo,250,8,9853,9907,10543,10636
-QK2TkEk5CeNvIzoo,350,8,9826,9924,10152,19277
-QK2TkEk5CeNvIzoo,450,8,9882,9909,9909,9908
-QK2TkEk5CeNvIzoo,550,8,9892,9907,9911,9911
-rX6DvPBTZFF6rBKU,50,2,8637,9930,10847,10308
-rX6DvPBTZFF6rBKU,150,2,9873,9920,11522,11831
-rX6DvPBTZFF6rBKU,200,2,9911,9913,10642,11232
-rX6DvPBTZFF6rBKU,250,2,9891,9912,10237,11721
-rX6DvPBTZFF6rBKU,350,2,9668,9913,10138,10001
-rX6DvPBTZFF6rBKU,450,2,9903,9912,10044,10044
-rX6DvPBTZFF6rBKU,550,2,9919,9945,9919,9919
-rX6DvPBTZFF6rBKU,50,4,5140,9913,14367,8030
-rX6DvPBTZFF6rBKU,150,4,9756,9908,9970,10675
-rX6DvPBTZFF6rBKU,200,4,9382,9914,9934,9706
-rX6DvPBTZFF6rBKU,250,4,9642,9913,9940,10094
-rX6DvPBTZFF6rBKU,350,4,8741,9977,9921,15849
-rX6DvPBTZFF6rBKU,450,4,9906,9910,9931,9932
-rX6DvPBTZFF6rBKU,550,4,9905,9907,9928,9929
-rX6DvPBTZFF6rBKU,50,6,5488,9971,11742,16137
-rX6DvPBTZFF6rBKU,150,6,7703,9957,12354,18646
-rX6DvPBTZFF6rBKU,200,6,9705,9910,10046,10181
-rX6DvPBTZFF6rBKU,250,6,9712,9909,9982,9973
-rX6DvPBTZFF6rBKU,350,6,9879,10025,9922,19810
-rX6DvPBTZFF6rBKU,450,6,9409,9910,9925,9893
-rX6DvPBTZFF6rBKU,550,6,9693,9908,9911,9858
-rX6DvPBTZFF6rBKU,50,8,5563,9909,18645,12367
-rX6DvPBTZFF6rBKU,150,8,9800,9910,11029,11524
-rX6DvPBTZFF6rBKU,200,8,8978,9909,10760,11837
-rX6DvPBTZFF6rBKU,250,8,8347,9907,10160,10197
-rX6DvPBTZFF6rBKU,350,8,9795,9911,9991,10536
-rX6DvPBTZFF6rBKU,450,8,9829,9971,9953,23754
-rX6DvPBTZFF6rBKU,550,8,9824,9923,10120,11284
-vrDZxzhxDsHrsosk,50,2,9828,9909,11710,11810
-vrDZxzhxDsHrsosk,150,2,9902,9951,11320,11107
-vrDZxzhxDsHrsosk,200,2,9892,9966,9907,9904
-vrDZxzhxDsHrsosk,250,2,9854,9917,10048,9909
-vrDZxzhxDsHrsosk,350,2,9844,10027,9910,11871
-vrDZxzhxDsHrsosk,450,2,9895,9908,9922,9924
-vrDZxzhxDsHrsosk,550,2,9896,9909,9990,9980
-vrDZxzhxDsHrsosk,50,4,6256,10028,14077,10299
-vrDZxzhxDsHrsosk,150,4,8810,9909,10191,9766
-vrDZxzhxDsHrsosk,200,4,8052,9970,10545,15252
-vrDZxzhxDsHrsosk,250,4,9709,9908,9941,10097
-vrDZxzhxDsHrsosk,350,4,9864,9909,9933,9921
-vrDZxzhxDsHrsosk,450,4,9900,9909,10121,10036
-vrDZxzhxDsHrsosk,550,4,9906,9913,9917,9913
-vrDZxzhxDsHrsosk,50,6,6625,9914,18986,12153
-vrDZxzhxDsHrsosk,150,6,9387,9908,10178,10299
-vrDZxzhxDsHrsosk,200,6,9526,10040,10558,19567
-vrDZxzhxDsHrsosk,250,6,9157,9909,10621,11068
-vrDZxzhxDsHrsosk,350,6,9575,9906,9942,9934
-vrDZxzhxDsHrsosk,450,6,9804,9909,9916,9933
-vrDZxzhxDsHrsosk,550,6,9745,9909,10052,10056
-vrDZxzhxDsHrsosk,50,8,5929,9909,19684,11089
-vrDZxzhxDsHrsosk,150,8,9854,9907,10779,11324
-vrDZxzhxDsHrsosk,200,8,9596,9971,11107,23702
-vrDZxzhxDsHrsosk,250,8,9648,9910,10364,10792
-vrDZxzhxDsHrsosk,350,8,9870,9909,9995,10323
-vrDZxzhxDsHrsosk,450,8,9444,9915,9938,10230
-vrDZxzhxDsHrsosk,550,8,9862,9909,9952,9946
-XLgh1cZBhXVMJQ0v,50,2,9064,10209,11841,10835
-XLgh1cZBhXVMJQ0v,150,2,9890,9912,9918,9903
-XLgh1cZBhXVMJQ0v,200,2,9903,9909,9930,9926
-XLgh1cZBhXVMJQ0v,250,2,9875,9921,9951,9980
-XLgh1cZBhXVMJQ0v,350,2,9827,9914,9942,9894
-XLgh1cZBhXVMJQ0v,450,2,9906,9914,10080,10086
-XLgh1cZBhXVMJQ0v,550,2,9828,9916,10481,9945
-XLgh1cZBhXVMJQ0v,50,4,3628,9920,15154,5618
-XLgh1cZBhXVMJQ0v,150,4,8554,9912,11370,10961
-XLgh1cZBhXVMJQ0v,200,4,8665,9911,10088,10150
-XLgh1cZBhXVMJQ0v,250,4,9824,9909,9914,9981
-XLgh1cZBhXVMJQ0v,350,4,9801,9909,10006,10031
-XLgh1cZBhXVMJQ0v,450,4,9871,9921,9930,15668
-XLgh1cZBhXVMJQ0v,550,4,9907,9907,9909,9907
-XLgh1cZBhXVMJQ0v,50,6,5008,9970,16381,12986
-XLgh1cZBhXVMJQ0v,150,6,9155,9907,10388,10636
-XLgh1cZBhXVMJQ0v,200,6,9894,9907,9946,9975
-XLgh1cZBhXVMJQ0v,250,6,9774,9911,9964,10457
-XLgh1cZBhXVMJQ0v,350,6,9332,9910,9939,10269
-XLgh1cZBhXVMJQ0v,450,6,9858,9968,9914,19779
-XLgh1cZBhXVMJQ0v,550,6,9606,10028,10010,19634
-XLgh1cZBhXVMJQ0v,50,8,6881,9920,21055,13458
-XLgh1cZBhXVMJQ0v,150,8,9872,9905,10408,10413
-XLgh1cZBhXVMJQ0v,200,8,9323,9908,10581,10535
-XLgh1cZBhXVMJQ0v,250,8,9255,9920,10398,14495
-XLgh1cZBhXVMJQ0v,350,8,7251,9970,10470,21946
-XLgh1cZBhXVMJQ0v,450,8,9234,9972,9956,15887
-XLgh1cZBhXVMJQ0v,550,8,9868,9910,9933,9924

r/tor_daemon.R

@@ -1,77 +0,0 @@
-library(ggplot2)
-library(sqldf)
-library(plyr)
-library(cowplot)
-
-cellssec <- c("1","2","10","20")
-tor_bw <- read.csv("tor_bw_2.csv")
-tor_bw <- sqldf("select run,conf,latency, CAST(latency as real) / 1000. as lat_ms from tor_bw")
-tor_bw$conf <- factor(
-  mapvalues(tor_bw$conf, c(0,1,2,3), cellssec),
-  levels = cellssec,
-  ordered = TRUE)
-gbw1 <- ggplot(data = tor_bw, aes(x = conf, y=lat_ms)) +
-  geom_boxplot() +
-  ylim(0,1000) +
-  ylab("latency (in ms)") +
-  xlab("cells / second") +
-  theme_minimal()
-
-gbw2 <- ggplot(data = tor_bw, aes(x = conf, y=lat_ms)) +
-  geom_boxplot() +
-  scale_y_log10() +
-  ylab("latency (in ms)") +
-  xlab("cells / second") +
-  theme_minimal()
-
-new_values <- c("1","2","3","4","5","6","7","8","∞")
-tor_guards <- read.csv("tor_guards.csv")
-tor_guards <- sqldf("select run,conf,latency, CAST(latency as real) / 1000. as lat_ms from tor_guards")
-tor_guards$conf <- factor(
-  mapvalues(tor_guards$conf, c(0,1,2,3,4,5,6,7,8), new_values),
-  levels = new_values,
-  ordered = TRUE)
-
-gguard1 <- ggplot(data = tor_guards, aes(x = conf, y=lat_ms)) +
-  geom_boxplot() +
-  ylim(0,1000) +
-  ylab("latency (in ms)") +
-  xlab("guards") +
-  theme_minimal()
-
-gguard2 <- ggplot(data = tor_guards, aes(x = conf, y=lat_ms)) +
-  geom_boxplot() +
-  scale_y_log10() +
-  ylab("latency (in ms)") +
-  xlab("guards") +
-  theme_minimal()
-
-relay_count <- c("6","4","3")
-tor_relays <- read.csv("tor_relays.csv")
-tor_relays <- sqldf("select run,conf,latency, CAST(latency as real) / 1000. as lat_ms from tor_relays")
-tor_relays$conf <- factor(
-  mapvalues(tor_relays$conf, c(0,1,2), relay_count),
-  levels = relay_count,
-  ordered = TRUE)
-
-grelay1 <- ggplot(data = tor_relays, aes(x = conf, y=lat_ms)) +
-  geom_boxplot() +
-  ylim(0,1000) +
-  #scale_y_log10() +
-  ylab("latency (in ms)") +
-  xlab("relay count") +
-  theme_minimal()
-
-grelay2 <- ggplot(data = tor_relays, aes(x = conf, y=lat_ms)) +
-  geom_boxplot() +
-  scale_y_log10() +
-  ylab("latency (in ms)") +
-  xlab("relay count") +
-  theme_minimal()
-
-t1 <- plot_grid(gbw1, gguard1, grelay1, labels = c('D', 'E', 'F'), nrow=1, rel_widths = c(5, 8, 4))
-t1 + ggsave("tor_latency_crop.png", dpi=300, dev='png', height=5, width=15, units="cm")
-
-t2 <- plot_grid(gbw2, gguard2, grelay2, labels = c('A', 'B', 'C'), nrow=1, rel_widths = c(5, 8, 4))
-t2 + ggsave("tor_latency_log.png", dpi=300, dev='png', height=5, width=15, units="cm")
-

r/tor_owd.R

@@ -1,32 +0,0 @@
-library(ggplot2)
-library(sqldf)
-library(dplyr)
-library(cowplot)
-
-tor_owd <- read.csv("tor_owd.csv")
-
-for (run_id in c(0:39)) {
-  print(run_id)
-  sel_owd <- filter(tor_owd, measure_type != "delta", measure_type != 'd_client_server', measure_type != 'd_server_client', measure_type != 'd_server_client_abs', measure_type != 'd_client_server_abs', run == paste("./out/nBV7DWLpj8WA6Be3-",run_id,sep=""))
-  ggplot(data = sel_owd, aes(x = packet_id, y=latency_ms, color=measure_type)) +
-    coord_cartesian(ylim=c(0,600)) +
-    geom_line() +
-    theme_classic() +
-    ggsave(paste("tor_owd_",run_id,".png", sep = ""), dpi=150, dev='png', height=18, width=32, units="cm")
-}
-
-ggplot(data = sqldf("select * from tor_owd where measure_type='delta_abs'"), aes(x=latency_ms)) +
-  stat_ecdf(pad = FALSE) +
-  coord_cartesian(xlim = c(0,500)) +
-  theme_classic()
-
-ggplot(data = sqldf("select * from tor_owd where measure_type='d_client_server_abs' or measure_type='d_server_client_abs'"), aes(x=latency_ms)) +
-  stat_ecdf(pad = FALSE) +
-  coord_cartesian(xlim = c(0,500)) +
-  theme_classic()
-
-ggplot(data = sqldf("select * from tor_owd where packet_id < 500 and run = './out/nBV7DWLpj8WA6Be3-16' and (measure_type='d_client_server' or measure_type='d_server_client')"), aes(y=latency_ms,x=packet_id,color=measure_type)) +
-  geom_line() +
-  coord_cartesian(ylim=c(-250,250)) +
-  geom_point() +
-  theme_classic()

src/dcall.c

@@ -4,6 +4,7 @@
 #include <glib-2.0/glib-object.h>
 #include <glib-2.0/glib-unix.h>
 #include <gst/rtp/gstrtpbuffer.h>
+#include <unistd.h>
 
 struct dcall_elements {
   GstElement *pipeline;
@@ -130,93 +131,6 @@ static GstPadProbeReturn jitter_buffer_sink_event(GstPad *pad, GstPadProbeInfo *
   return GST_PAD_PROBE_OK;
 }
 
-static GstPadProbeReturn jitter_buffer_sink_event_up(GstPad *pad, GstPadProbeInfo *info, gpointer user_data) {
-  g_print("upstream event: begin\n");
-  jitter_buffer_sink_event (pad, info, user_data);
-  g_print("upstream event: end\n");
-  return GST_PAD_PROBE_OK;
-}
-
-static GstPadProbeReturn jitter_buffer_sink_event_down(GstPad *pad, GstPadProbeInfo *info, gpointer user_data) {
-  g_print("downstream event: begin\n");
-  jitter_buffer_sink_event (pad, info, user_data);
-  g_print("downstream event: end\n");
-  return GST_PAD_PROBE_OK;
-}
-
-static gboolean foreach_buffer (GstBuffer *inbuf, GstMeta **meta, gpointer user_data) {
-  //@FIXME Dead code, please remove me
-  return TRUE;
-}
-
-static GstPadProbeReturn extract_buffer(GstPad *pad, GstPadProbeInfo *info, gpointer user_data) {
-  //g_print("Entering rtpjitterbuffer sink pad handler for buffers...\n");
-  struct dcall_elements *de = user_data;
-  guint16 seqnum;
-  GstBuffer *out = NULL, *rtp_extracted = NULL;
-  //GstRTPBuffer rtp_buffer = {0};
-  GstMapInfo map;
-
-  out = gst_pad_probe_info_get_buffer (info);
-  if (out == NULL) {
-    g_print("Empty buffer \n");
-    return GST_PAD_PROBE_OK;
-  }
-
-  if (GST_BUFFER_FLAG_IS_SET (out, GST_BUFFER_FLAG_DISCONT)) {
-    g_print("Discontinuous buffer\n");
-  }
-  return GST_PAD_PROBE_OK;
-  //gst_buffer_map (out, &map, GST_MAP_READ);
-
-  //if (gst_buffer_get_size (out) != 172)  g_print("buffer size is %ld\n", gst_buffer_get_size (out));
-/*
-  if (G_UNLIKELY (!gst_rtp_buffer_map (out, GST_MAP_READ, &rtp_buffer))) return GST_PAD_PROBE_OK;
-  seqnum = gst_rtp_buffer_get_seq (&rtp_buffer);
-
-  if (de->prev_seqnum == 0) de->prev_seqnum = seqnum;
-  else if (de->prev_seqnum + 1 == seqnum) de->prev_seqnum = seqnum;
-  else {
-    g_print("Current seqnum = %d, previous seqnum = %d, gap\n", seqnum, de->prev_seqnum);
-    de->prev_seqnum = seqnum;
-  }
-
-  g_print("Receiving packet %d\n", seqnum);
-
-  rtp_extracted = gst_rtp_buffer_get_payload_buffer (&rtp_buffer);
-
-  gst_buffer_foreach_meta (rtp_extracted, foreach_buffer, NULL);
-
-  gst_rtp_buffer_unmap (&rtp_buffer);*/
-}
-
-void register_pad(struct dcall_elements *de) {
-  GstPad *pad_src, *pad_opusdec;
-  pad_src = gst_element_get_static_pad (de->rx_jitterbuffer, "src");
-  pad_opusdec = gst_element_get_static_pad (de->rx_opusdec, "src");
-
-  //gst_pad_add_probe (pad_src, GST_PAD_PROBE_TYPE_EVENT_DOWNSTREAM, jitter_buffer_sink_event_down, &de, NULL);
-  //gst_pad_add_probe (pad_src, GST_PAD_PROBE_TYPE_EVENT_UPSTREAM, jitter_buffer_sink_event_up, &de, NULL);
-  gst_pad_add_probe (pad_opusdec, GST_PAD_PROBE_TYPE_BUFFER, extract_buffer, &de, NULL);
-}
-
-static gboolean pipeline_bus_handler (GstBus *bus, GstMessage *message, gpointer data) {
-  g_print ("Got %s message\n", GST_MESSAGE_TYPE_NAME (message));
-
-  const GstStructure *struc = NULL;
-  struc = gst_message_get_structure(message);
-  g_print("structure is: %s \n", gst_structure_to_string (struc));
-
-  return TRUE;
-}
-
-void register_bus(struct dcall_elements *de) {
-  GstBus *bus;
-  bus = gst_pipeline_get_bus (GST_PIPELINE (de->pipeline));
-  de->bus_watch_id = gst_bus_add_watch (bus, pipeline_bus_handler, de);
-  gst_object_unref(bus);
-}
-
 gboolean stop_handler(gpointer user_data) {
   GMainLoop *loop = user_data;
   g_main_loop_quit(loop);
@@ -249,9 +163,6 @@ int main(int argc, char *argv[]) {
   if (create_rx_chain (&de) != 0) return -1;
   if (create_tx_chain (&de) != 0) return -1;
 
-  register_pad(&de);
-  //register_bus(&de);
-
   gst_element_set_state (de.pipeline, GST_STATE_PLAYING);
 
   g_unix_signal_add (SIGTERM, stop_handler, loop);
@@ -275,7 +186,6 @@ int main(int argc, char *argv[]) {
   gst_element_set_state (de.pipeline, GST_STATE_NULL);
 
   gst_object_unref (GST_OBJECT (de.pipeline));
-  // g_source_remove (de.bus_watch_id);
   g_main_loop_unref (loop);
 
   return 0;