Unrealistic CPU graphs - "rrdtool tune" not working

[aawanderley]

Hello,

First of all, we are running Cacti 0.8.8a on a Centos 5.8 server.
While researching why the CPU usage graphs for two RHEL 6 boxes were not matching the "top" output, I came upon the solution of running "rrdtool tune" to set the proper max value for cpu_user, cpu_system and cpu_nice.

As those servers have 8 CPU with 4 cores, I run:
rrdtool tune /var/www/html/cacti/rra/kia_cpu_user_1668.rrd --maximum cpu_user:3200
rrdtool tune /var/www/html/cacti/rra/kia_cpu_system_1667.rrd --maximum cpu_system:3200
rrdtool tune /var/www/html/cacti/rra/kia_cpu_nice_1666.rrd --maximum cpu_nice:3200

Here's my problem (and please forgive my lack of understanding, if the answer is right in from of me)...:
When I run the rrdtool tune for the first server, it fixed the graphs right away, they're looking good now.
But when I run for the second server, its graphs didn't change at all. And I did check with "rrdtool info" to confirm that ".max" was set properly (see below)
That server's CPU usage is still being plotted no problem, but still not matching what is reported by "top". Why is that...?

Thanks a lot,

Alex

/# rrdtool info /var/www/html/cacti/rra/kia_cpu_system_1667.rrd
filename = "/var/www/html/cacti/rra/kia_cpu_system_1667.rrd"
rrd_version = "0003"
step = 300
last_update = 1394560811
header_size = 2040
ds[cpu_system].index = 0
ds[cpu_system].type = "COUNTER"
ds[cpu_system].minimal_heartbeat = 600
ds[cpu_system].min = 0.0000000000e+00
ds[cpu_system].max = 3.2000000000e+03
ds[cpu_system].last_ds = "50568362"
ds[cpu_system].value = 7.7110000000e+01
ds[cpu_system].unknown_sec = 0
rra[0].cf = "AVERAGE"
rra[0].rows = 600
rra[0].cur_row = 43
rra[0].pdp_per_row = 1
rra[0].xff = 5.0000000000e-01
rra[0].cdp_prep[0].value = NaN
rra[0].cdp_prep[0].unknown_datapoints = 0
rra[1].cf = "AVERAGE"
rra[1].rows = 700
rra[1].cur_row = 624
rra[1].pdp_per_row = 6
rra[1].xff = 5.0000000000e-01
rra[1].cdp_prep[0].value = 0.0000000000e+00
rra[1].cdp_prep[0].unknown_datapoints = 0
rra[2].cf = "AVERAGE"
rra[2].rows = 775
rra[2].cur_row = 619
rra[2].pdp_per_row = 24
rra[2].xff = 5.0000000000e-01
rra[2].cdp_prep[0].value = 0.0000000000e+00
rra[2].cdp_prep[0].unknown_datapoints = 0
rra[3].cf = "AVERAGE"
rra[3].rows = 797
rra[3].cur_row = 712
rra[3].pdp_per_row = 288
rra[3].xff = 5.0000000000e-01
rra[3].cdp_prep[0].value = 1.0853381937e+03
rra[3].cdp_prep[0].unknown_datapoints = 0
rra[4].cf = "MAX"
rra[4].rows = 600
rra[4].cur_row = 441
rra[4].pdp_per_row = 1
rra[4].xff = 5.0000000000e-01
rra[4].cdp_prep[0].value = NaN
rra[4].cdp_prep[0].unknown_datapoints = 0
rra[5].cf = "MAX"
rra[5].rows = 700
rra[5].cur_row = 584
rra[5].pdp_per_row = 6
rra[5].xff = 5.0000000000e-01
rra[5].cdp_prep[0].value = -inf
rra[5].cdp_prep[0].unknown_datapoints = 0
rra[6].cf = "MAX"
rra[6].rows = 775
rra[6].cur_row = 666
rra[6].pdp_per_row = 24
rra[6].xff = 5.0000000000e-01
rra[6].cdp_prep[0].value = -inf
rra[6].cdp_prep[0].unknown_datapoints = 0
rra[7].cf = "MAX"
rra[7].rows = 797
rra[7].cur_row = 656
rra[7].pdp_per_row = 288
rra[7].xff = 5.0000000000e-01
rra[7].cdp_prep[0].value = 5.0210007209e+01
rra[7].cdp_prep[0].unknown_datapoints = 0