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library ( ggplot2 )
library ( sqldf )
library ( plyr )
library ( cowplot )
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thunder_ms <- read.csv ( " thunder_configure_17.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 )
thunder_ms $ jmax <- as.factor ( thunder_ms $ jmax )
v1 <- ggplot ( data = thunder_ms , aes ( x = jmax , y = lat_ms , fill = links ) ) +
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#geom_violin() +
geom_boxplot ( outlier.size = 0.1 ) +
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geom_hline ( yintercept = 800 , color = " red" , linetype = " dashed" ) +
geom_hline ( yintercept = 400 , color = " DarkGreen" ) +
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annotate ( " text" , x = 7 , y = 550 , label = " high" , color = " DarkGreen" ) +
annotate ( " text" , x = 7 , y = 1000 , label = " acceptable" , color = " red" ) +
coord_cartesian ( xlim = c ( 1 , 7 ) ) +
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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)" ) +
xlab ( " max allowed jitter" ) +
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,(9900-COUNT(latency)*1.0)/9900 as dlv from thunder_ms group by jmax,links,run" )
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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 ) ) +
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geom_boxplot ( outlier.size = 0.1 ) +
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scale_y_continuous ( labels = scales :: percent ) +
scale_fill_grey ( ) +
ylab ( " dropped packets" ) +
xlab ( " max allowed jitter" ) +
theme_classic ( )
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thunder_bw <- read.csv ( " thunder_configure_17_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" )
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 ) ) +
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geom_boxplot ( outlier.size = 0.1 ) +
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#scale_y_log10() +
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#scale_y_log10(labels = scales::percent) +
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" )
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thunder_links <- read.csv ( " thunder_configure_16_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" )
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 )
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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" ) +
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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 (
"
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 ( ) +
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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 ( )
gobal_links <- sqldf (
"
select ts , run , durations_global , 8 - 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 ) +
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ylab ( " Cumulated downtime" ) +
xlab ( " Number of links down at once" ) +
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theme_classic ( )
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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 ( ) +
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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" ) +
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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select sorting , lat_ms , ident , tm.jmax , tm.links
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from
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
tm.run = sel_run.run and tm.jmax = sel_run.jmax and tm.links = sel_run.links
" )
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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 ( " Call duration" ) +
theme_classic ( )
thunder_drop <- read.csv ( " thunder_configure_16_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 '%-24' " )
#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 ( " Call duration" ) +
scale_fill_grey ( ) +
theme_classic ( )
thunder_drop_burst <- read.csv ( " thunder_configure_16_drop_burst.csv" )
tdb_ag <- sqldf ( " select run,count,COUNT(count) as oc from thunder_drop_burst where run LIKE '%-24' group by run,count" )
tdb_ag_2 <- sqldf ( " select run,count,oc,row_number() OVER (partition by count order by run) as sorting from tdb_ag" )
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 , fill = sorting ) ) +
geom_bar ( stat = " summary" , position = " dodge" ) +
#grom_bar() +
#scale_y_log10() +
scale_y_continuous ( ) +
ylab ( " Occurence" ) +
xlab ( " Packets dropped in a row" ) +
scale_fill_grey ( ) +
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theme_classic ( )
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thunder_red <- read.csv ( " thunder_configure_16_red.csv" )
tred <- sqldf ( " select * from thunder_red WHERE run LIKE '%-24'" )
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t3 <- plot_grid ( v8 , v9 , v10 , labels = c ( ' A' , ' B' , ' C' ) , ncol = 2 )