metrics: add memory used line plot to PDF, update description

Author: askervin

metrics/report/report_dockerfile/collectd_scaling.R

@@ -430,6 +430,7 @@ for (currentdir in resultdirs) {
 # It's nice to show the graphs in Gb, at least for any decent sized test
 # run, so make a new column with that pre-divided data in it for us to use.
 memfreedata$mem_free_gb = memfreedata$value/(1024*1024*1024)
+memuseddata$mem_used_gb = memuseddata$value/(1024*1024*1024)
 # And show the boot times in seconds, not ms
 podbootdata$launch_time_s = podbootdata$launch_time/1000.0
 
@@ -440,8 +441,9 @@ mem_stats_plot = suppressWarnings(ggtexttable(data.frame(memstats),
 	rows=NULL
 	))
 
-mem_scale = (max(memfreedata$value) / (1024*1024*1024)) / max(podbootdata$n_pods)
-mem_line_plot <- ggplot() +
+mem_free_scale = (max(memfreedata$value) / (1024*1024*1024)) / max(podbootdata$n_pods)
+mem_used_scale = (max(memuseddata$value) / (1024*1024*1024)) / max(podbootdata$n_pods)
+mem_free_line_plot <- ggplot() +
 	geom_line(data=memfreedata,
 			  aes(s_offset, mem_free_gb, colour=interaction(testname, node),
 				  group=interaction(testname, node)),
@@ -451,20 +453,43 @@ mem_line_plot <- ggplot() +
 				   group=interaction(testname, node)),
 			   alpha=0.5, size=0.5) +
 	geom_line(data=podbootdata,
-			  aes(x=s_offset, y=n_pods*mem_scale, colour=interaction(testname,"pod count"), group=testname),
+			  aes(x=s_offset, y=n_pods*mem_free_scale, colour=interaction(testname,"pod count"), group=testname),
 			  alpha=0.2) +
 	geom_point(data=podbootdata,
-			   aes(x=s_offset, y=n_pods*mem_scale, colour=interaction(testname,"pod count"), group=testname),
+			   aes(x=s_offset, y=n_pods*mem_free_scale, colour=interaction(testname,"pod count"), group=testname),
 			   alpha=0.3, size=0.5) +
 	labs(colour="") +
 	xlab("seconds") +
 	ylab("System Avail (Gb)") +
-	scale_y_continuous(labels=comma, sec.axis=sec_axis(~ ./mem_scale, name="pods")) +
+	scale_y_continuous(labels=comma, sec.axis=sec_axis(~ ./mem_free_scale, name="pods")) +
 	ggtitle("System Memory free") +
 	theme(axis.text.x=element_text(angle=90))
 
+mem_used_line_plot <- ggplot() +
+	geom_line(data=memuseddata,
+			  aes(s_offset, mem_used_gb, colour=interaction(testname, node),
+				  group=interaction(testname, node)),
+			  alpha=0.3) +
+	geom_point(data=memuseddata,
+			   aes(s_offset, mem_used_gb, colour=interaction(testname, node),
+				   group=interaction(testname, node)),
+			   alpha=0.5, size=0.5) +
+	geom_line(data=podbootdata,
+			  aes(x=s_offset, y=n_pods*mem_used_scale, colour=interaction(testname,"pod count"), group=testname),
+			  alpha=0.2) +
+	geom_point(data=podbootdata,
+			   aes(x=s_offset, y=n_pods*mem_used_scale, colour=interaction(testname,"pod count"), group=testname),
+			   alpha=0.3, size=0.5) +
+	labs(colour="") +
+	xlab("seconds") +
+	ylab("System Used (Gb)") +
+	scale_y_continuous(labels=comma, sec.axis=sec_axis(~ ./mem_used_scale, name="pods")) +
+	ggtitle("System Memory used, not counting Cached, Buffered and SLAB") +
+	theme(axis.text.x=element_text(angle=90))
+
 page1 = grid.arrange(
-	mem_line_plot,
+	mem_free_line_plot,
+	mem_used_line_plot,
 	mem_stats_plot,
 	ncol=1
 	)

metrics/report/report_dockerfile/metrics_report.Rmd

@@ -40,7 +40,7 @@ source('parallel.R')
 
 # Runtime scaling rapid
 This [test](https://github.com/clearlinux/cloud-native-setup/metrics/scaling/k8s_scale_fast.sh)
-uses collectd to asynchronously measure CPU idle %, free memory, pod boot time, free inodes,
+uses collectd to asynchronously measure CPU idle %, free and used memory, pod boot time, free inodes,
 and interface stats as it launches more and more idle `busybox` pods on a Kubernetes cluster.
 
 > Note: CPU % is measured as a system whole - 100% represents *all* CPUs on the node.