--- /dev/null
+slices <- function (x, components=FALSE)
+{
+ m<-x$memsize;
+ d<-x$disksize/250;
+ c<-x$cpuspeed;
+ r<-x$numcores;
+ if ( components ) {
+ a<-c(m,d,c*r);
+ } else {
+ a<-(m+d+c*r);
+ }
+ return(a/2);
+}
+
+slices_2 <- function (x, components=FALSE)
+{
+ # Define an ideal, then scale each measurement relative to the ideal.
+ # If it matches it will be more or less than 1
+ # does this scale (up or down) linearly, and why not?
+
+ # 4, 2.4x2, 1000; 4, 3.2x1, 320; 1, 2.4x1, 160
+ ideal_m <- 3.4; # GB
+ ideal_c <- 2.4; # GHz
+ ideal_d <- 450; # GB
+ ideal_r <- 2;
+
+ m<-x$memsize/ideal_m;
+ d<-x$disksize/ideal_d;
+ c<-x$cpuspeed/ideal_c;
+ r<-x$numcores/ideal_r;
+ # ideal is 1
+
+ if ( components ) {
+ a<-c(m,d,c*r);
+ } else {
+ a<-(m+d+c*r);
+ }
+
+ return (a/3*5);
+}
+
+slices_3 <- function (x, components=FALSE)
+{
+ # Define an ideal, then scale each measurement relative to the ideal.
+ # If it matches it will be more or less than 1
+ # does this scale (up or down) linearly, and why not?
+
+ # 4, 2.4x2, 1000; 4, 3.2x1, 320; 1, 2.4x1, 160
+ ideal_m <- 3.4; #GB
+ ideal_c <- 2.4; #GHz
+ ideal_d <- 450; #GB
+ ideal_r <- 2;
+ ideal_bw <- 100000; #Kbps
+
+ m<-x$memsize/ideal_m;
+ d<-x$disksize/ideal_d;
+ c<-x$cpuspeed/ideal_c;
+ r<-x$numcores/ideal_r;
+ b<-log(x$bwlimit)/log(ideal_bw);
+ # ideal is 1
+
+ if ( components ) {
+ a<-c(m,d,c*r,b);
+ } else {
+ a<-(m+d+c*r+b);
+ }
+
+ return (a/4*5);
+}
+
+slices_4 <- function (x, components=FALSE)
+{
+ # Define an ideal, then scale each measurement relative to the ideal.
+ # If it matches it will be more or less than 1
+ # does this scale (up or down) linearly, and why not?
+
+ # 4, 2.4x2, 1000; 4, 3.2x1, 320; 1, 2.4x1, 160
+ ideal_m <- 3.4; #GB
+ ideal_c <- 2.4; #GHz
+ ideal_d <- 450; #GB
+ ideal_r <- 2;
+ ideal_bw <- 100000; #Kbps
+ ideal_pcu <- 1;
+
+ m<-x$memsize/ideal_m;
+ d<-x$disksize/ideal_d;
+ c<-x$cpuspeed/ideal_c;
+ r<-x$numcores/ideal_r;
+ b<-log(x$bwlimit)/log(ideal_bw);
+ p<-x$pcustatus/ideal_pcu;
+ # ideal is 1
+
+ if ( components ) {
+ a<-c(m,d,c*r,b,p);
+ } else {
+ a<-(m+d+c*r+b+p);
+ }
+
+ return (a/5*5);
+}
+
+index_of_bin <- function (h, value)
+{
+ index <- 0;
+
+ for (i in sequence(length(h$breaks)))
+ {
+ # first bin
+
+ if ( value < h$breaks[1] )
+ {
+ index <- 1;
+ break;
+ }
+
+ # last bin
+
+ if ( i == length(h$breaks) )
+ {
+ # end of line
+ index <- i;
+ break;
+ }
+
+ # all other bins
+
+ if ( value > h$breaks[i] && value <= h$breaks[i+1] )
+ {
+ index <- i+1;
+ break;
+ }
+ }
+ if ( index == 0 ) {
+ warning("index == 0, no bin assigned for value: ", value);
+ }
+
+ return (index);
+}
+
+start_image <- function (name, width=480, height=480)
+{
+ png(name, width=width, height=height);
+}
+
+end_image <- function ()
+{
+ dev.off()
+}
+
+
+plot_rt_hist <- function (t, imagename=0)
+{
+ d2 <- (t$lastreply - t$start)
+ std_dev <- sd(log(d2))
+ m <- mean(log(d2))
+ print(sprintf("mean: %s, stddev: %s\n", m, std_dev));
+
+ if ( imagename != 0 ) { start_image(imagename) }
+
+ h<-hist(log(d2),
+ xlab="Hours between ticket creation and final reply",
+ main="Time to Final Reply for RT Tickets", axes=FALSE)
+
+ a<-exp(h$breaks)/(60*60) # convert units from log(secs) to hours
+ axis(1,labels=signif(a,2), at=h$breaks)
+ axis(2)
+
+ x<-seq(min(h$breaks),max(h$breaks),length=500)
+ y<-dnorm(x,mean=m, sd=std_dev)
+
+ # scale y to the size of h's 'counts' vector rather than the density function
+ lines(x,y*max(h$counts)/max(y))
+ if ( imagename != 0 ) { end_image() }
+}
+
+year_hist <- function (t, year, from, to, max, type="week", title="Histogram for Tickets in")
+{
+ dates <-seq(as.Date(from), as.Date(to), type)
+ months <- format(dates, "%b-%d")
+ hbreaks<-unclass(as.POSIXct(dates))
+ h<-hist(t$start, breaks=hbreaks, plot=FALSE)
+ main<-sprintf(paste(title, "%s: MEAN %s\n"), year, mean(h$counts))
+ print(main);
+ if ( max == 0 ) {
+ max = max(h$counts)
+ }
+ plot(h, ylim=c(0,max), main=main, axes=FALSE)
+ axis(1, labels=months, at=hbreaks)
+ axis(2)
+ abline(mean(h$counts), 0, col='grey')
+ #qqnorm(h$counts)
+ #qqline(h$counts)
+}
+
+year_hist_unique <- function (t, year, from, to, max, type="week", title="Histogram for Tickets in")
+{
+ dates <-seq(as.Date(from), as.Date(to), type)
+ months <- format(dates, "%b-%d")
+ hbreaks<-unclass(as.POSIXct(dates))
+
+ rows <- NULL
+ for ( d in hbreaks )
+ {
+ d_end <- d+60*60*24
+ t_sub <- t[which(t$start > d & t$start <= d_end),]
+ rows <- rbind(rows, c('start'=d, 'reboots'=length(unique(t_sub$hostname))) )
+ }
+ rows <- data.frame(rows)
+
+ if ( max == 0 ) {
+ max = max(rows$reboots)
+ }
+ main<-sprintf(paste(title, "%s: MEAN %s\n"), year, mean(rows$reboots))
+ print(main);
+ barplot(rows$reboots, ylim=c(0,max), main=main, axes=FALSE, space=0)
+ #plot(h, ylim=c(0,max), main=main, axes=FALSE)
+ axis(1, labels=months, at=seq(1,length(hbreaks)))
+ axis(2)
+ abline(mean(rows$reboots), 0, col='grey')
+ #qqnorm(h$counts)
+ #qqline(h$counts)
+ return (rows);
+}
+
+year_hist_unique_recent <- function (t, year, from, to, max, blocks=c(1,3,7,14,30), type="week", title="Histogram for Tickets in")
+{
+ dates <-seq(as.Date(from), as.Date(to), type)
+ months <- format(dates, "%b-%d")
+ hbreaks<-unclass(as.POSIXct(dates))
+
+ rows <- NULL
+
+
+ for ( d in hbreaks )
+ {
+ # initialize row for this iteration
+ row <- NULL
+ row[as.character(0)] <- 0
+ for ( block in blocks ) {
+ row[as.character(block)] <- 0
+ }
+
+ # find the range : d plus a day
+ d_end <- d+60*60*24
+ # find unique hosts in this day range
+ t_sub <- t[which(t$start > d & t$start <= d_end),]
+ unique_hosts <- unique(t_sub$hostname)
+ if (length(unique_hosts) == 0 ) {
+ rows <- rbind(rows, c('start'=d, row))
+ next
+ }
+
+ #print(sprintf("unique_hosts: %s\n", unique_hosts));
+ print(sprintf("unique_hosts: %s\n", length(unique_hosts)));
+
+ for ( host in as.character(unique_hosts) )
+ {
+ found <- 0
+ for ( block in blocks )
+ {
+ #print(sprintf("date: %s, block: -%s, %s\n", d, block, host));
+ #print(sprintf("row: %s\n", row));
+ # find the range : 'block' days ago to 'd'
+ d_back <- d - 60*60*24 * block
+ t_back_sub <- t[which(t$start > d_back & t$start <= d),]
+ u <- unique(t_back_sub$hostname)
+ if ( length(u[u==host]) >= 1)
+ {
+ # add to block_count and go to next host.
+ found <- 1
+ i <- as.character(block)
+ row[i] <- row[i] + 1
+ break
+ }
+ }
+ if ( found == 0 )
+ {
+ # no range found
+ row['0'] <- row['0'] + 1
+ }
+ }
+ rows <- rbind(rows, c('start'=d, row))
+ }
+
+ rows <- data.frame(rows)
+
+ if ( max == 0 ) {
+ max = max(rows['0'])
+ }
+ #main<-sprintf(paste(title, "%s: MEAN %s\n"), year, mean(rows$reboots))
+ #print(main);
+ #barplot(rows$reboots, ylim=c(0,max), main=main, axes=FALSE, space=0)
+ ##plot(h, ylim=c(0,max), main=main, axes=FALSE)
+ #axis(1, labels=months, at=seq(1,length(hbreaks)))
+ #axis(2)
+ #abline(mean(rows$reboots), 0, col='grey')
+ #qqnorm(h$counts)
+ #qqline(h$counts)
+ return (rows);
+}
+
+source("myImagePlot.R")
+reboot_image <- function (t, year, from, to, max=0, type="week", title="")
+{
+ dates <-seq(as.Date(from), as.Date(to), type)
+ months <- format(dates, "%b-%d")
+ hbreaks<-unclass(as.POSIXct(dates))
+
+ rows <- NULL
+ image <- matrix(data=0, nrow=max(as.numeric(t$hostname)), ncol=length(hbreaks))
+ #image <- matrix(data=0, nrow=length(unique(t$hostname)), ncol=length(hbreaks))
+
+ #for ( d in hbreaks )
+ for ( i in seq(1, length(hbreaks)) )
+ {
+ # find the range : d plus a day
+ d <- hbreaks[i]
+ d_end <- d+60*60*24
+ # find unique hosts in this day range
+ t_sub <- t[which(t$start > d & t$start <= d_end),]
+ unique_hosts <- unique(t_sub$hostname)
+ if (length(unique_hosts) == 0 ) { next }
+
+ for ( host in unique_hosts )
+ {
+ image[host,i] <- 1
+ }
+ }
+
+ myImagePlot(image, xLabels=months, yLabels=c(""), title=title)
+
+ #found <- 0
+ #for ( block in blocks )
+ #{
+ #print(sprintf("date: %s, block: -%s, %s\n", d, block, host));
+ #print(sprintf("row: %s\n", row));
+ # find the range : 'block' days ago to 'd'
+ # d_back <- d - 60*60*24 * block
+ # t_back_sub <- t[which(t$start > d_back & t$start <= d),]
+ # u <- unique(t_back_sub$hostname)
+ # if ( length(u[u==host]) >= 1)
+ # {
+ # # add to block_count and go to next host.
+ # found <- 1
+ # i <- as.character(block)
+ # row[i] <- row[i] + 1
+ # break
+ # }
+ #}
+ #if ( found == 0 )
+ #{
+ # # no range found
+ # row['0'] <- row['0'] + 1
+ #}
+ #}
+ #rows <- rbind(rows, c('start'=d, row))
+
+ #rows <- data.frame(rows)
+
+ #if ( max == 0 ) {
+ # max = max(rows['0'])
+ #}
+ #main<-sprintf(paste(title, "%s: MEAN %s\n"), year, mean(rows$reboots))
+ #print(main);
+ #barplot(rows$reboots, ylim=c(0,max), main=main, axes=FALSE, space=0)
+ ##plot(h, ylim=c(0,max), main=main, axes=FALSE)
+ #axis(1, labels=months, at=seq(1,length(hbreaks)))
+ #axis(2)
+ #abline(mean(rows$reboots), 0, col='grey')
+ #qqnorm(h$counts)
+ #qqline(h$counts)
+ return (image);
+}
+
+add_year <- function (t)
+{
+ t$year <- c(0) # assign new column with zero value initially
+ for ( i in 1:length(t$start) )
+ {
+ d <- as.POSIXlt(t$start[i], origin="1970-01-01")
+ year <- d$year + 1900 # as.numeric(format(d, "%Y"))
+ t$year[i] <- year
+ }
+ return (t);
+}
+
+add_timestamp <- function (t)
+{
+ t$start <- c(0) # assign new column with zero value initially
+ for ( i in 1:length(t$date) )
+ {
+ tstamp <-unclass(as.POSIXct(t$date[i], origin="1970-01-01"))[1]
+ t$start[i] <- tstamp
+ }
+ return (t);
+}
+
+abline_at_date <- function (date, col='black', lty=1, format="%Y-%m-%d")
+{
+ ts <-unclass(as.POSIXct(date, format=format, origin="1970-01-01"))[1]
+ abline(v=ts, col=col, lty=lty)
+ return (ts);
+}
git://git.onelab.eu / monitor.git / blobdiff