X-Git-Url: http://git.onelab.eu/?p=monitor.git;a=blobdiff_plain;f=statistics%2Fbm_reboot_unique.r;fp=statistics%2Fbm_reboot_unique.r;h=29f2bf6ee00949ed3726f6d386b115c6a528854d;hp=0000000000000000000000000000000000000000;hb=5a40e4f30c45995dcbd06c5f9a7b5f997c4c926c;hpb=76aa61d5a7f5ec00f7cb571094ebda835211fdd0

diff --git a/statistics/bm_reboot_unique.r b/statistics/bm_reboot_unique.r
new file mode 100644
index 0000000..29f2bf6
--- /dev/null
+++ b/statistics/bm_reboot_unique.r
@@ -0,0 +1,250 @@
+
+source("functions.r");
+
+# system("parse_rt_data.py > rt_data.csv");
+# ./bmevents.py events.1-18-10 BootUpdateNode > bm_reboot_2010-01-18.csv
+# ./bmevents.py events.10-08-09 BootUpdateNode > bm_reboot_2009-10-08.csv 
+# ./bmevents.py events.29.12.08.dump BootUpdateNode > bm_reboot_2008-12-29.csv
+# ./bmevents.py events.8-25-09.dump BootUpdateNode > bm_reboot_2009-08-25.csv
+# 
+bm <- read.csv('bm_reboot.csv', sep=',', header=TRUE)
+bm_api <- read.csv('bm_reboot_2008-12-29.csv', sep=',', header=TRUE)
+
+bm2<-bm
+
+tstamp_78 <-unclass(as.POSIXct("2008-01-01", origin="1960-01-01"))[1]
+tstamp_89 <-unclass(as.POSIXct("2009-01-01", origin="1960-01-01"))[1]
+
+bm_7 <- bm2[which( bm2$start < tstamp_78 ),]
+bm_8 <- bm2[which( bm2$start >= tstamp_78 & bm2$start < tstamp_89 ),]
+bm_9 <- bm2[which( bm2$start >= tstamp_89 ),]
+
+tstamp <-unclass(as.POSIXct("2008-01-01", origin="1960-01-01"))
+bm_67 <- bm2[which( bm2$start <  tstamp[1] ),]
+bm_89 <- bm2[which( bm2$start >= tstamp[1] ),]
+
+
+#start_image("bm_reboot.png")
+
+par(mfrow=c(2,1))
+par(mai=c(.5,.4,.5,.4))
+#year_hist(bm_9, "2009", "2009/06/21", "2010/2/10", 500, 'day', "Daily Reboot Rates")
+#rows <- year_hist_unique(bm_9, "2009", "2009/06/21", "2010/2/10", 100, 'day', "Unique Daily Reboots")
+#end_image()
+
+if ( TRUE )
+{
+    rows_blocks <- year_hist_unique_recent(bm_9, "2009", "2009/06/21", "2010/2/10", 100, c(1,3,7,14,30), 'day', "Unique Daily Reboots")
+
+    x<-NULL
+    blocks <- c(0,1,3,7,14,30)
+    for ( b in blocks ) { x<- c(x, paste("X", b, sep="")) }
+
+    par(mfrow=c(1,1))
+    par(mai=c(1,.7,.5,.4))
+    start_image("bm_reboot_color.png", width=900)
+
+    barplot(t(rows_blocks[x]), border=NA, col=c('purple', 'blue', 'green', 'red', 'pink', 'orange', 'yellow'), ylim=c(0,100), main="How Recently Node were Rebooted", xlab="Days from June-2009 to Jan-2010", space=0, legend=c("Only today", "Also within 1 day", "Also within 3 days", "Also within 7 days", "Also within 14 days", "Also within 30 days"), ylab="Frequency")
+    end_image()
+
+    #barplot(rows_blocks$X0, border=NA, col=c('purple', 'blue', 'green', 'red', 'pink', 'orange', 'yellow'), ylim=c(0,100))
+
+    #par(mfrow=c(6,1))
+    #par(mai=c(.1,.7,.1,.1))
+    #barplot(rows_blocks$X0, border=NA, col=c('purple'), ylim=c(0,100))
+    #barplot(rows_blocks$X1, border=NA, col=c('blue'), ylim=c(0,100))
+    #barplot(rows_blocks$X3, border=NA, col=c('green'), ylim=c(0,100))
+    #barplot(rows_blocks$X7, border=NA, col=c('red'), ylim=c(0,100))
+    #barplot(rows_blocks$X14, border=NA, col=c('pink'), ylim=c(0,100))
+    #barplot(rows_blocks$X30, border=NA, col=c('orange'), ylim=c(0,100))
+
+    shapiro.test(rows_blocks$X0[ rows_blocks$X0 < 50 ])
+    shapiro.test(rows_blocks$X1[ rows_blocks$X1 < 50 ])
+    shapiro.test(rows_blocks$X3[ rows_blocks$X3 < 50 ])
+    shapiro.test(rows_blocks$X7[ rows_blocks$X7 < 50 ])
+    shapiro.test(rows_blocks$X14[ rows_blocks$X14 < 50 ])
+    shapiro.test(rows_blocks$X30[ rows_blocks$X30 < 50 ])
+}
+
+
+#image <- reboot_image(t_9, "2009", "2009/06/21", "2010/2/10", 0, 'day')
+#myImagePlot(image)
+
+start_image("st_bm_reboots.png", width=400, height=600)
+image <- reboot_image(bm_9, "2009", "2009/06/21", "2010/2/10", 0, 'day', title="BootManager Reboots for all Nodes")
+end_image()
+
+start_image("st_api_event_reboots.png", width=800, height=600)
+image2 <- reboot_image(bm_api, "2009", "2008/06/21", "2010/2/10", 0, 'day', title= "API Reboot Events for all Nodes")
+end_image()
+
+reboot_frequency <- function ( img )
+{
+    d <- dim(img)
+    # for each row
+    f <- NULL
+    for ( i in seq(1:d[1]) )
+    {
+        r <- img[i,]
+        f <- c(f, sum(r))
+    }
+    return (f);
+}
+
+reboot_events <- function ( img )
+{
+    d <- dim(img)
+    # for each row
+    f <- NULL
+    for ( i in seq(1:d[2]) )
+    {
+        c <- img[,i]
+        f <- c(f, sum(c))
+    }
+    return (f);
+}
+
+time_to_reboot <- function (img, first=0, last=0)
+{
+    d <- dim(img)
+    # for each row
+    f <- NULL
+    for ( i in seq(1:d[1]) )
+    {
+        if (last == 0 ) { last <- length(img[i,]) }
+        r <- img[i,first:last]
+        # find  first reboot
+        start_i <- 1
+        while ( start_i < length(r) && r[start_i] != 1 ) 
+        { 
+            start_i <- start_i + 1 
+        }
+        end_i <- start_i
+
+        while ( start_i < length(r) )
+        {
+            if ( r[start_i] == 1 && start_i != end_i)
+            {
+                f <- c(f, start_i-end_i)
+                while ( start_i < length(r) && r[start_i] == 1 ) { start_i <- start_i + 1 }
+                end_i <- start_i
+            }
+            start_i <- start_i + 1
+        }
+    }
+    return (f);
+}
+
+find_95 <- function (cdf, low=0, high=1000) 
+{
+    # find the lowest point past the 95th percentile.
+    while ( high - low > 1)
+    {
+        c_low <- cdf(low)
+        c_mid <- cdf(low+floor((high-low)/2))
+        c_high <- cdf(high)
+
+        c_min <- min(min(abs(0.95-c_low), abs(0.95-c_mid)), abs(0.95-c_high))
+
+        if ( c_mid > 0.95 ) {
+            high <- high - floor((high-low)/2)
+            print (sprintf("adjust high: %s\n", high));
+        } else if ( c_mid <= 0.95 ) {
+            low <- low + floor((high-low)/2)
+            print (sprintf("adjust low: %s\n", low));
+        }
+
+        #swap<-0
+        #if ( c_min == abs(0.95-c_mid) ) {
+        #    # is it in top half or bottom half?
+        #    print (sprintf("middle\n"));
+        #    if ( abs(0.95-c_low) < abs(0.95-c_high) ) {
+        #        low <- low + floor((high-low)/2)
+        #        print (sprintf("adjust low: %s\n", low));
+        #    } else { #if ( c_min == abs(0.95-c_high) ) {
+        #        high <- high - floor((high-low)/2)
+        #        print (sprintf("adjust high: %s\n", high));
+        #    }
+        #} else {
+        #    if ( c_min == abs(0.95-c_low) ) {
+        #        high <- high - floor((high-low)/2)
+        #        print (sprintf("adjust high: %s\n", high));
+        #    } else { #if ( c_min == abs(0.95-c_high) ) {
+        #        low <- low + floor((high-low)/2)
+        #        print (sprintf("adjust low: %s\n", low));
+        #    }
+        #}
+    }
+    return (low)
+}
+
+#0,193-402,length(r)
+ttr1 <- time_to_reboot(image,9,122)
+ttr2 <- time_to_reboot(image,131,223)
+
+ttr8 <- time_to_reboot(image2,0,193)
+ttr9 <- time_to_reboot(image2,402)
+
+x1 <- ecdf(c(ttr1, ttr2))
+x2 <- ecdf(c(ttr8,ttr9))
+start_image("reboot_ttr_cdf.png")
+plot(x1, col.vert='red', col.hor="red", col.points="red", pch='*', xlab="Days to Reboot", ylab="Percentile", verticals=TRUE, xlim=c(0,170), main="CDF of Days to Reboot for BM & API Events")
+plot(x2, col.vert='blue', col.hor="blue", col.points="blue", pch=20, verticals=TRUE, add=TRUE)
+legend(130, 0.15, legend=c("BM Uploads", "API Events"), col=c('red', 'blue'), pch=c(42, 20))
+abline(0.95,0)
+v1<-find_95(x1)
+v2<-find_95(x2)
+abline(v=v1, col="pink")
+abline(v=v2, col="light blue")
+axis(1, labels=c(v1,v2), at=c(v1,v2))
+
+abline(v=7, col="grey")
+abline(v=14, col="grey")
+abline(v=21, col="grey")
+abline(v=28, col="grey")
+abline(v=42, col="grey")
+abline(v=56, col="grey")
+end_image()
+
+e <- reboot_events(image)
+e2 <- reboot_events(image2)
+x1 <- ecdf(e)
+x2 <- ecdf(e2)
+
+start_image("reboot_days_cdf.png")
+plot(x1, col.vert='red', col.hor="red", col.points="red", pch='*', xlab="Reboots in a Single Day", ylab="Percentile", verticals=TRUE, xlim=c(0,100), main="CDF of Reboots per Day for BM & API Events")
+plot(x2, col.vert='blue', col.hor="blue", col.points="blue", pch=20, verticals=TRUE, add=TRUE)
+legend(75, 0.15, legend=c("BM Uploads", "API Events"), col=c('red', 'blue'), pch=c(42, 20))
+abline(0.95,0)
+v1<-find_95(x1)
+v2<-find_95(x2)
+abline(v=v1, col="pink")
+abline(v=v2, col="light blue")
+axis(1, labels=c(v1,v2), at=c(v1,v2))
+end_image()
+
+
+
+f <- reboot_frequency(image)
+f2 <- reboot_frequency(image2)
+x1 <- ecdf(f)
+x2 <- ecdf(f2)
+
+start_image("reboot_node_cdf.png")
+par(mfrow=c(1,1))
+par(mai=c(.9,.8,.5,.4))
+plot(x1, col.vert='red', col.hor="red", col.points="red", pch='*', xlab="Reboots per Node", ylab="Percentile", verticals=TRUE, xlim=c(0,100), main="CDF of Reboot per Node for BM & API Events")
+plot(x2, col.vert='blue', col.hor="blue", col.points="blue", pch=20, verticals=TRUE, add=TRUE)
+legend(75, 0.15, legend=c("BM Uploads", "API Events"), col=c('red', 'blue'), pch=c(42, 20))
+abline(0.95,0)
+v1<-find_95(x1)
+v2<-find_95(x2)
+abline(v=v1, col="pink")
+abline(v=v2, col="light blue")
+axis(1, labels=c(v1,v2), at=c(v1,v2))
+end_image()
+
+
+
+par(mfrow=c(1,1))
+par(mai=c(.7,.7,.7,.7))