Re: chooseleaf_descend_once
Hey Jim, Running the third test with tunable chooseleaf_descend_once 0 with no devices marked out yields the following result (999.827397, 0.48667056652539997) so chi squared value is 999 with a corresponding p value of 0.487 so that the placement distribution seems to be drawn from the uniform distribution as desired. Caleb -- To unsubscribe from this list: send the line unsubscribe ceph-devel in the body of a message to majord...@vger.kernel.org More majordomo info at http://vger.kernel.org/majordomo-info.html
Re: chooseleaf_descend_once
On 11/28/2012 09:11 AM, Caleb Miles wrote:
Hey Jim,
Running the third test with tunable chooseleaf_descend_once 0 with no
devices marked out yields the following result
(999.827397, 0.48667056652539997)
so chi squared value is 999 with a corresponding p value of 0.487 so that
the placement distribution seems to be drawn from the uniform distribution
as desired.
Great, thanks for doing that extra test.
Plus, I see that Sage has merged it. Cool.
Thanks -- Jim
Caleb
On Tue, Nov 27, 2012 at 1:28 PM, Jim Schuttjasc...@sandia.gov wrote:
Hi Caleb,
On 11/26/2012 07:28 PM, caleb miles wrote:
Hello all,
Here's what I've done to try and validate the new chooseleaf_descend_once
tunable first described in commit f1a53c5e80a48557e63db9c52b83f3**9391bc69b8
in the wip-crush branch of ceph.git.
First I set the new tunable to it's legacy value, disabled,
tunable choose_local_tries 0
tunable choose_local_fallback_tries 0
tunable choose_total_tries 50
tunable chooseleaf_descend_once 0
The map contains one thousand osd devices contained in one hundred hosts
with the following data rule
rule data {
ruleset 0
type replicated
min_size 1
max_size 10
step take default
step chooseleaf firstn 0 type host
step emit
}
I then simulate the creation of one million placement groups using the
crushtool
$ crushtool -i hundred.map --test --min-x 0 --max-x 99 --num-rep 3
--output-csv --weight 120 0.0 --weight 121 0.0 --weight 122 0.0 --weight
123 0.0 --weight 124 0.0 --weight 125 0.0 --weight 125 0.0 --weight 150 0.0
--weight 151 0.0 --weight 152 0.0 --weight 153 0.0 --weight 154 0.0
--weight 155 0.0 --weight 156 0.0 --weight 180 0.0 --weight 181 0.0
--weight 182 0.0 --weight 183 0.0 --weight 184 0.0 --weight 185 0.0
--weight 186 0.0
with the majority of devices in three hosts marked out. Then in (I)Python
import scipy.stats as s
import matplotlib.mlab as m
data = m.csv2rec(data-device_**utilization.csv)
s.chisquare(data['number_of_**objects_stored'], data['number_of_objects_*
*expected'])
which will output
(122939.76474477499, 0.0)
so that the chi squared value is 122939.795 and the p value is, rounded
to, 0.0 and the observed placement distribution statistically differs from
a uniform distribution. Repeating with the new tunable set to
tunable chooseleaf_descend_once 1
I obtain the following result
(998.97643161876761, 0.32151775131589833)
so that the chi squared value is 998.976 and the p value is 0.32 and the
observed placement distribution is statistically identical to the uniform
distribution at the five and ten percent confidence levels, higher as well
of course. The p value is the probability of obtaining a chi squared value
more extreme than the statistic observed. Basically, from my rudimentary
understanding of probability theory, that if you obtain a p value p P
then reject the null hypothesis, in our case that the observed placement
distribution is drawn from the uniform distribution, at the P confidence
level.
Cool. Thanks for doing these tests.
Is there any point to doing a third test, with
tunable chooseleaf_descend_once 0
and no devices marked out, but in all other respects
the same as the above two tests?
I would expect the results for that case and the last
case you tested to be essentially identical in the degree
of uniformity, but is it worth verifying?
-- Jim
Caleb
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Re: chooseleaf_descend_once
Hi Caleb,
On 11/26/2012 07:28 PM, caleb miles wrote:
Hello all,
Here's what I've done to try and validate the new chooseleaf_descend_once
tunable first described in commit f1a53c5e80a48557e63db9c52b83f39391bc69b8 in
the wip-crush branch of ceph.git.
First I set the new tunable to it's legacy value, disabled,
tunable choose_local_tries 0
tunable choose_local_fallback_tries 0
tunable choose_total_tries 50
tunable chooseleaf_descend_once 0
The map contains one thousand osd devices contained in one hundred hosts with
the following data rule
rule data {
ruleset 0
type replicated
min_size 1
max_size 10
step take default
step chooseleaf firstn 0 type host
step emit
}
I then simulate the creation of one million placement groups using the crushtool
$ crushtool -i hundred.map --test --min-x 0 --max-x 99 --num-rep 3
--output-csv --weight 120 0.0 --weight 121 0.0 --weight 122 0.0 --weight 123
0.0 --weight 124 0.0 --weight 125 0.0 --weight 125 0.0 --weight 150 0.0
--weight 151 0.0 --weight 152 0.0 --weight 153 0.0 --weight 154 0.0 --weight
155 0.0 --weight 156 0.0 --weight 180 0.0 --weight 181 0.0 --weight 182 0.0
--weight 183 0.0 --weight 184 0.0 --weight 185 0.0 --weight 186 0.0
with the majority of devices in three hosts marked out. Then in (I)Python
import scipy.stats as s
import matplotlib.mlab as m
data = m.csv2rec(data-device_utilization.csv)
s.chisquare(data['number_of_objects_stored'],
data['number_of_objects_expected'])
which will output
(122939.76474477499, 0.0)
so that the chi squared value is 122939.795 and the p value is, rounded to, 0.0
and the observed placement distribution statistically differs from a uniform
distribution. Repeating with the new tunable set to
tunable chooseleaf_descend_once 1
I obtain the following result
(998.97643161876761, 0.32151775131589833)
so that the chi squared value is 998.976 and the p value is 0.32 and the observed
placement distribution is statistically identical to the uniform distribution at
the five and ten percent confidence levels, higher as well of course. The p value
is the probability of obtaining a chi squared value more extreme than the
statistic observed. Basically, from my rudimentary understanding of probability
theory, that if you obtain a p value p P then reject the null hypothesis, in
our case that the observed placement distribution is drawn from the uniform
distribution, at the P confidence level.
Cool. Thanks for doing these tests.
Is there any point to doing a third test, with
tunable chooseleaf_descend_once 0
and no devices marked out, but in all other respects
the same as the above two tests?
I would expect the results for that case and the last
case you tested to be essentially identical in the degree
of uniformity, but is it worth verifying?
-- Jim
Caleb
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To unsubscribe from this list: send the line unsubscribe ceph-devel in
the body of a message to majord...@vger.kernel.org
More majordomo info at http://vger.kernel.org/majordomo-info.html
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chooseleaf_descend_once
Hello all,
Here's what I've done to try and validate the new
chooseleaf_descend_once tunable first described in commit
f1a53c5e80a48557e63db9c52b83f39391bc69b8 in the wip-crush branch of
ceph.git.
First I set the new tunable to it's legacy value, disabled,
tunable choose_local_tries 0
tunable choose_local_fallback_tries 0
tunable choose_total_tries 50
tunable chooseleaf_descend_once 0
The map contains one thousand osd devices contained in one hundred hosts
with the following data rule
rule data {
ruleset 0
type replicated
min_size 1
max_size 10
step take default
step chooseleaf firstn 0 type host
step emit
}
I then simulate the creation of one million placement groups using the
crushtool
$ crushtool -i hundred.map --test --min-x 0 --max-x 99 --num-rep 3
--output-csv --weight 120 0.0 --weight 121 0.0 --weight 122 0.0 --weight
123 0.0 --weight 124 0.0 --weight 125 0.0 --weight 125 0.0 --weight 150
0.0 --weight 151 0.0 --weight 152 0.0 --weight 153 0.0 --weight 154 0.0
--weight 155 0.0 --weight 156 0.0 --weight 180 0.0 --weight 181 0.0
--weight 182 0.0 --weight 183 0.0 --weight 184 0.0 --weight 185 0.0
--weight 186 0.0
with the majority of devices in three hosts marked out. Then in (I)Python
import scipy.stats as s
import matplotlib.mlab as m
data = m.csv2rec(data-device_utilization.csv)
s.chisquare(data['number_of_objects_stored'],
data['number_of_objects_expected'])
which will output
(122939.76474477499, 0.0)
so that the chi squared value is 122939.795 and the p value is, rounded
to, 0.0 and the observed placement distribution statistically differs
from a uniform distribution. Repeating with the new tunable set to
tunable chooseleaf_descend_once 1
I obtain the following result
(998.97643161876761, 0.32151775131589833)
so that the chi squared value is 998.976 and the p value is 0.32 and the
observed placement distribution is statistically identical to the
uniform distribution at the five and ten percent confidence levels,
higher as well of course. The p value is the probability of obtaining a
chi squared value more extreme than the statistic observed. Basically,
from my rudimentary understanding of probability theory, that if you
obtain a p value p P then reject the null hypothesis, in our case that
the observed placement distribution is drawn from the uniform
distribution, at the P confidence level.
Caleb
--
To unsubscribe from this list: send the line unsubscribe ceph-devel in
the body of a message to majord...@vger.kernel.org
More majordomo info at http://vger.kernel.org/majordomo-info.html
