417 lines
15 KiB
R
417 lines
15 KiB
R
library(ggplot2)
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library(sqldf)
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library(plyr)
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library(cowplot)
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thunder_ms <- read.csv("thunder_configure_100ms.csv")
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thunder_ms <- sqldf("select run,ident,jmax,links,latency, CAST(latency as real) / 1000. as lat_ms from thunder_ms")
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thunder_ms$links <- as.factor(thunder_ms$links)
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thunder_ms$jmax <- as.factor(thunder_ms$jmax)
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v1 <- ggplot(data = thunder_ms, aes(x = jmax, y=lat_ms, fill=links)) +
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#geom_violin() +
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geom_boxplot(outlier.size=0.1) +
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geom_hline(yintercept=800, color = "red", linetype="dashed") +
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geom_hline(yintercept=400, color = "DarkGreen") +
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annotate("text", x=7.1, y=650, label= "high", color="DarkGreen") +
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annotate("text", x=7, y=1200, label= "acceptable", color="red") +
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coord_cartesian(xlim = c(1, 7), ylim = c(0, 1000)) +
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#ylim(0,1000) +
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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("max allowed jitter") +
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theme_classic()
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#v1 + ggsave("thunder_configure_ms.png", dpi=300, dev='png', height=5, width=15, units="cm")
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thunder_rcv <- sqldf("select run,jmax,links,(3000-COUNT(latency)*1.0)/3000 as dlv from thunder_ms group by jmax,links,run")
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thunder_rcv$jmax <- as.factor(thunder_rcv$jmax)
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thunder_rcv$links <- as.factor(thunder_rcv$links)
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v2 <- ggplot(data = thunder_rcv, aes(x = jmax, y=dlv, fill=links)) +
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geom_boxplot(outlier.size=0.1) +
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#geom_violin(scale='width') +
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scale_y_continuous(labels = scales::percent) +
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scale_fill_grey() +
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#scale_y_log10(labels = scales::percent) +
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ylab("dropped packets") +
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xlab("max allowed jitter") +
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theme_classic()
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thunder_bw <- read.csv("thunder_configure_100ms_bw.csv")
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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")
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thunder_bw$jmax <- as.factor(thunder_bw$jmax)
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thunder_bw$links <- as.factor(thunder_bw$links)
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v3 <- ggplot(data = thunder_bw, aes(x = jmax, y=sent_ratio, fill=links)) +
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geom_boxplot(outlier.size=0.1) +
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#scale_y_log10() +
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coord_cartesian(ylim = c(1,2)) +
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scale_fill_grey() +
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ylab("bandwidth ratio") +
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xlab("max allowed jitter") +
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theme_classic()
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t1 <- plot_grid(v1, v2, v3, labels = c('A', 'B', 'C'), ncol=1)
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t1 + ggsave("thunder_configure.png", dpi=300, dev='png', height=15, width=15, units="cm")
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thunder_links <- read.csv("thunder_configure_20_links.csv")
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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")
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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")
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links_down_at_least_once2$sorting <- as.factor(links_down_at_least_once2$sorting)
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v4 <- ggplot(data = links_down_at_least_once2, aes(x = sorting, y=down_ratio)) +
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#geom_violin() +
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#geom_boxplot(width=0.2) +
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#scale_y_log10() +
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#scale_y_log10(labels = scales::percent) +
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geom_bar(stat="summary") +
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#scale_y_log10() +
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scale_y_continuous(labels = scales::percent) +
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scale_fill_grey() +
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ylab("Cumulated downtime") +
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xlab("Sorted links") +
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theme_classic()
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downtime <- sqldf(
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"
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select sorting,1.0*duration/1000 as dur
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from thunder_links as tl
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inner join links_down_at_least_once2 as l2 on
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tl.run = l2.run
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and tl.link_id = l2.link_id
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where will_change='True'
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and status='down'")
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v5 <- ggplot(data = downtime, aes(x=sorting, y=dur)) +
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geom_violin() +
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geom_boxplot(width=0.1, outlier.shape = NA) +
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scale_y_log10() +
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ylab("Downtime (in sec)") +
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xlab("Sorted links") +
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theme_classic()
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gobal_links <- sqldf(
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"
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select ts,run,durations_global,12-COUNT(status) as down_link_count,xp_time
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from thunder_links
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where will_change_global='True' and status='up' and durations_global > 0
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group by ts,run,durations_global
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")
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down_group_ratio <- sqldf(
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"
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select run,1.0 * SUM(durations_global)/xp_time as down_ratio, down_link_count
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from gobal_links
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where down_link_count >= 0
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group by run,down_link_count,xp_time
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")
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down_group_ratio$down_link_count <- as.factor(down_group_ratio$down_link_count)
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v6 <- ggplot(data = down_group_ratio, aes(x=down_link_count, y=down_ratio)) +
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geom_bar(stat="summary") +
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#scale_y_log10() +
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scale_y_continuous(labels = scales::percent) +
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ylab("Cumulated downtime") +
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xlab("Number of links down at once") +
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theme_classic()
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downtime_group <- sqldf(
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"
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select down_link_count, 1.0*durations_global/1000 as dur from gobal_links where down_link_count >= 0
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")
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downtime_group$down_link_count <- as.factor(downtime_group$down_link_count)
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v7 <- ggplot(data = downtime_group, aes(x=down_link_count, y=dur)) +
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geom_violin() +
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geom_boxplot(width=0.1, outlier.shape = NA) +
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scale_y_log10() +
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ylab("Downtime (in sec)") +
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xlab("Number of links down at once") +
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theme_classic()
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t2 <- plot_grid(v4, v5, v6, v7, labels = c('A', 'B', 'C', 'D'), ncol=2)
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t2 + ggsave("thunder_links.png", dpi=300, dev='png', height=12, width=15, units="cm")
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latency_evol <- sqldf(
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"
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select sorting,lat_ms,ident,tm.jmax,tm.links
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from
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thunder_ms as tm,
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(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
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where
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tm.run = sel_run.run and tm.jmax = sel_run.jmax and tm.links = sel_run.links
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")
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latency_evol$sorting <- as.factor(latency_evol$sorting)
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v8 <- ggplot(data=latency_evol, aes(x=ident,y=lat_ms)) +
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geom_line() +
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xlab("Packet identifier") +
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ylab("Latency (ms)") +
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theme_classic()
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thunder_drop <- read.csv("thunder_configure_partial_drop.csv")
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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' ")
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#cats <- c("0-989","990-1979","1980-2969","2970-3959","3960-4949","4950-5939","5940-6929","6930-7919","7920-8909","8910-9899")
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thunder_drop_2$packet_range <- as.factor(thunder_drop_2$packet_range)
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thunder_drop_2$sorting <- as.factor(thunder_drop_2$sorting)
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#thunder_drop_2$packet_range <- factor(
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# mapvalues(thunder_drop_2$packet_range, cats, cats),
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# levels = cats,
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# ordered = TRUE)
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v9 <- ggplot(data = thunder_drop_2, aes(x=packet_range, y=packet_ratio,fill=sorting)) +
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geom_bar(stat="summary",position = "dodge") +
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#grom_bar() +
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#scale_y_log10() +
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scale_y_continuous(labels = scales::percent) +
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ylab("Packets dropped") +
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xlab("Packet identifier") +
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labs(fill="Run") +
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scale_fill_grey() +
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theme_classic() +
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theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.key.size = unit(0.2, "cm"))
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thunder_drop_burst <- read.csv("thunder_configure_partial_drop_burst.csv")
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tdb_ag <- sqldf("select run,count,COUNT(count) as oc from thunder_drop_burst where run LIKE '%-23' group by run,count")
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tdb_ag_2 <- sqldf(
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"
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select
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td.run as r,
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count,
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oc,
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total,
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1.0 * oc / total as oc_ratio,
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row_number() OVER (partition by count order by td.run) as sorting
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from
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tdb_ag as td,
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(select run,SUM(oc) as total from tdb_ag group by run) as ag
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where
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td.run = ag.run
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")
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tdb_ag_2$sorting <- as.factor(tdb_ag_2$sorting)
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tdb_ag_2$count <- as.factor(tdb_ag_2$count)
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v10 <- ggplot(data = tdb_ag_2, aes(x=count, y=oc_ratio)) +
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#geom_bar(stat="summary",position = "dodge") +
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#scale_y_log10() +
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geom_violin(scale='width') +
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geom_boxplot(width=0.1, outlier.shape=NA) +
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scale_y_continuous(labels = scales::percent) +
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ylab("% observed drops") +
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xlab("Packets lost during the drop") +
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scale_fill_grey() +
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theme_classic()
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thunder_red <- read.csv("thunder_configure_partial_red.csv")
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tred <- sqldf(
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"
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select
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tr.run as r,
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delivered_at_once,
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1.0 * occur / total as occur_ratio,
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occur,
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total,
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row_number() OVER (partition by delivered_at_once order by tr.run) as sorting
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from
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thunder_red tr,
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(select run,SUM(occur) as total from thunder_red group by run) as ag
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WHERE
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tr.run LIKE '%-23'
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and tr.run = ag.run
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")
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tred$sorting <- as.factor(tred$sorting)
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tred$delivered_at_once <- as.factor(tred$delivered_at_once)
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v11 <- ggplot(data = tred, aes(x=delivered_at_once, y=occur_ratio)) +
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#geom_bar(stat="summary",position = "dodge") +
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geom_violin(scale='width') +
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xlab('Fresh packets per cell') +
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ylab('% of received cells') +
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scale_y_continuous(labels = scales::percent) +
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geom_boxplot(width=0.1, outlier.shape=NA) +
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theme_classic()
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t3 <- plot_grid(v8, v9, v10, v11, labels = c('A', 'B', 'C', 'D'), ncol=2)
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t3 + ggsave("thunder_packets.png", dpi=300, dev='png', height=12, width=15, units="cm")
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tor_multi_lat <- read.csv("tor_just_many_latencies.csv")
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tor_multi_lat_100 <- read.csv("tor_just_many_latencies_100.csv")
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tor_drop <- sqldf(
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"
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select run,conf,1.0*MAX(ident)/33 as last_one,'33' as pkt_sec from tor_multi_lat group by run,conf
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union
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select run,conf,1.0*MAX(ident)/10 as last_one, '10' as pkt_sec from tor_multi_lat_100 group by run,conf
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")
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v12 <- ggplot(data=tor_drop,aes(x=last_one, linetype=factor(pkt_sec, levels=c('33', '10')))) +
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stat_ecdf(pad = FALSE) +
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ylab("% broken links") +
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labs(linetype="Pkt/sec") +
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coord_cartesian(xlim = c(0, 300), ylim = c(0,0.5)) +
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scale_y_continuous(labels = scales::percent) +
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xlab("Elapsed time (sec)") +
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theme_classic()
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v12 + ggsave("broken.png", dpi=300, dev='png', height=5, width=15, units="cm")
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library(dplyr)
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library(purrr)
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library(tidyr)
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tor_lat_stack <- tor_multi_lat %>%
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dplyr::mutate(latency = latency / 1000) %>%
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dplyr::group_by(run,conf) %>%
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dplyr::summarise(
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id = paste(first(run),first(conf)),
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min = min(latency),
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q25 = quantile(latency,0.25) - min(latency),
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median = median(latency) - quantile(latency,0.25),
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q75 = quantile(latency,0.75) - median(latency),
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q95 = quantile(latency,0.95) - quantile(latency,0.75),
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q99 = quantile(latency,0.99) - quantile(latency,0.95),
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max = max(latency) - quantile(latency,0.99),
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max_sort = max(latency),
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median_sort = median(latency)
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)
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tor_lat_stack <- gather(tor_lat_stack, 'min', 'max', 'q25', 'median', 'q75', 'q95', 'q99', key="quantile_name", value="quantile_value")
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v13 <- ggplot(tor_lat_stack, aes(
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x=reorder(id,median_sort),
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y=quantile_value,
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fill=factor(quantile_name, levels=c('max','q99','q95','q75', 'median', 'q25', 'min')))
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) +
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coord_cartesian(ylim = c(0,1500)) +
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labs(fill="quantile")+
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xlab("Tor circuits") +
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ylab("RTT (ms)") +
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geom_bar(stat="identity", position="stack",width=1) +
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scale_fill_grey() +
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theme_classic() +
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theme(axis.text.x=element_blank(), axis.ticks.x = element_blank(), legend.key.size = unit(0.2, "cm"))
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v14 <- ggplot(tor_lat_stack, aes(
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x=reorder(id,median_sort),
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y=quantile_value,
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fill=factor(quantile_name, levels=c('max','q99','q95','q75', 'median', 'q25', 'min')))
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) +
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#coord_cartesian(ylim = c(0,1500)) +
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labs(fill="quantile")+
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xlab("Tor circuits") +
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ylab("RTT (ms)") +
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geom_bar(stat="identity", position="stack",width=1) +
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scale_fill_grey() +
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theme_classic() +
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theme(axis.text.x=element_blank(),axis.ticks.x = element_blank(), legend.key.size = unit(0.2, "cm"))
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v15 <- ggplot(tor_lat_stack, aes(
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x=reorder(id,max_sort),
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y=quantile_value,
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fill=factor(quantile_name, levels=c('max','q99','q95','q75', 'median', 'q25', 'min')))
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) +
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coord_cartesian(ylim = c(0,1500)) +
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labs(fill="quantile")+
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xlab("Tor circuits") +
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ylab("RTT (ms)") +
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geom_bar(stat="identity", position="stack",width=1) +
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scale_fill_grey() +
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theme_classic() +
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theme(axis.text.x=element_blank(), axis.ticks.x = element_blank(), legend.key.size = unit(0.2, "cm"), plot.tag.position='top')
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v16 <- ggplot(tor_lat_stack, aes(
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x=reorder(id,max_sort),
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y=quantile_value,
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fill=factor(quantile_name, levels=c('max','q99','q95','q75', 'median', 'q25', 'min')))
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) +
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#coord_cartesian(ylim = c(0,1500)) +
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labs(fill="quantile")+
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xlab("Tor circuits") +
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ylab("RTT (ms)") +
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geom_bar(stat="identity", position="stack",width=1) +
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scale_fill_grey() +
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theme_classic() +
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theme(axis.text.x=element_blank(), axis.ticks.x = element_blank(), legend.key.size = unit(0.2, "cm"),plot.tag.position='bottom')
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t4 <- plot_grid(v16, v15, v14, v13, labels = c('A', 'B', 'C', 'D'), ncol=1)
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t4 + ggsave("tor_30ms.png", dpi=300, dev='png', height=20, width=15, units="cm")
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tor_lat_stack_100 <- tor_multi_lat_100 %>%
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dplyr::mutate(latency = latency / 1000) %>%
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dplyr::group_by(run,conf) %>%
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dplyr::summarise(
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id = paste(first(run),first(conf)),
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min = min(latency),
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q25 = quantile(latency,0.25) - min(latency),
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median = median(latency) - quantile(latency,0.25),
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q75 = quantile(latency,0.75) - median(latency),
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q95 = quantile(latency,0.95) - quantile(latency,0.75),
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q99 = quantile(latency,0.99) - quantile(latency,0.95),
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max = max(latency) - quantile(latency,0.99),
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max_sort = max(latency),
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median_sort = median(latency)
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)
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tor_lat_stack_100 <- gather(tor_lat_stack_100, 'min', 'max', 'q25', 'median', 'q75', 'q95', 'q99', key="quantile_name", value="quantile_value")
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v17 <- ggplot(tor_lat_stack_100, aes(
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x=reorder(id,median_sort),
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y=quantile_value,
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fill=factor(quantile_name, levels=c('max','q99','q95','q75', 'median', 'q25', 'min')))
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) +
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coord_cartesian(ylim = c(0,1500)) +
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labs(fill="quantile")+
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xlab("Tor circuits") +
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ylab("RTT (ms)") +
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geom_bar(stat="identity", position="stack",width=1) +
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scale_fill_grey() +
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theme_classic() +
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theme(axis.text.x=element_blank(), axis.ticks.x = element_blank(), legend.key.size = unit(0.2, "cm"))
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v18 <- ggplot(tor_lat_stack_100, aes(
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x=reorder(id,median_sort),
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y=quantile_value,
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fill=factor(quantile_name, levels=c('max','q99','q95','q75', 'median', 'q25', 'min')))
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) +
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#coord_cartesian(ylim = c(0,1500)) +
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labs(fill="quantile")+
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xlab("Tor circuits") +
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ylab("RTT (ms)") +
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geom_bar(stat="identity", position="stack",width=1) +
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scale_fill_grey() +
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theme_classic() +
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theme(axis.text.x=element_blank(),axis.ticks.x = element_blank(), legend.key.size = unit(0.2, "cm"))
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v19 <- ggplot(tor_lat_stack_100, aes(
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x=reorder(id,max_sort),
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y=quantile_value,
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fill=factor(quantile_name, levels=c('max','q99','q95','q75', 'median', 'q25', 'min')))
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) +
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coord_cartesian(ylim = c(0,1500)) +
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labs(fill="quantile")+
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xlab("Tor circuits") +
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|
ylab("RTT (ms)") +
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|
geom_bar(stat="identity", position="stack",width=1) +
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|
scale_fill_grey() +
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|
theme_classic() +
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|
theme(axis.text.x=element_blank(), axis.ticks.x = element_blank(), legend.key.size = unit(0.2, "cm"), plot.tag.position='top')
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|
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|
v20 <- ggplot(tor_lat_stack_100, aes(
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|
x=reorder(id,max_sort),
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|
y=quantile_value,
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|
fill=factor(quantile_name, levels=c('max','q99','q95','q75', 'median', 'q25', 'min')))
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|
) +
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|
#coord_cartesian(ylim = c(0,1500)) +
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|
labs(fill="quantile")+
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|
xlab("Tor circuits") +
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|
ylab("RTT (ms)") +
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|
geom_bar(stat="identity", position="stack",width=1) +
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|
scale_fill_grey() +
|
|
theme_classic() +
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|
theme(axis.text.x=element_blank(), axis.ticks.x = element_blank(), legend.key.size = unit(0.2, "cm"),plot.tag.position='bottom')
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|
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t5 <- plot_grid(v20, v19, v18, v17, labels = c('A', 'B', 'C', 'D'), ncol=1)
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t5 + ggsave("tor_100ms.png", dpi=300, dev='png', height=20, width=15, units="cm")
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|
|