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.. SPDX-License-Identifier: GPL-2.0
=================================================
DAMON Moniting Interval Parameters Tuning Example
=================================================
DAMON's monitoring parameters need tuning based on given workload and the
monitoring purpose. There is a :ref:`tuning guide
<damon_design_monitoring_params_tuning_guide>` for that. This document
provides an example tuning based on the guide.
Setup
=====
For below example, DAMON of Linux kernel v6.11 and `damo
<https://github.com/damonitor/damo>`_ (DAMON user-space tool) v2.5.9 was used to
monitor and visualize access patterns on the physical address space of a system
running a real-world server workload.
5ms/100ms intervals: Too Short Interval
=======================================
Let's start by capturing the access pattern snapshot on the physical address
space of the system using DAMON, with the default interval parameters (5
milliseconds and 100 milliseconds for the sampling and the aggregation
intervals, respectively). Wait ten minutes between the start of DAMON and
the capturing of the snapshot, to show a meaningful time-wise access patterns.
::
# damo start
# sleep 600
# damo record --snapshot 0 1
# damo stop
Then, list the DAMON-found regions of different access patterns, sorted by the
"access temperature". "Access temperature" is a metric representing the
access-hotness of a region. It is calculated as a weighted sum of the access
frequency and the age of the region. If the access frequency is 0 %, the
temperature is multiplied by minus one. That is, if a region is not accessed,
it gets minus temperature and it gets lower as not accessed for longer time.
The sorting is in temperature-ascendint order, so the region at the top of the
list is the coldest, and the one at the bottom is the hottest one. ::
# damo report access --sort_regions_by temperature
0 addr 16.052 GiB size 5.985 GiB access 0 % age 5.900 s # coldest
1 addr 22.037 GiB size 6.029 GiB access 0 % age 5.300 s
2 addr 28.065 GiB size 6.045 GiB access 0 % age 5.200 s
3 addr 10.069 GiB size 5.983 GiB access 0 % age 4.500 s
4 addr 4.000 GiB size 6.069 GiB access 0 % age 4.400 s
5 addr 62.008 GiB size 3.992 GiB access 0 % age 3.700 s
6 addr 56.795 GiB size 5.213 GiB access 0 % age 3.300 s
7 addr 39.393 GiB size 6.096 GiB access 0 % age 2.800 s
8 addr 50.782 GiB size 6.012 GiB access 0 % age 2.800 s
9 addr 34.111 GiB size 5.282 GiB access 0 % age 2.300 s
10 addr 45.489 GiB size 5.293 GiB access 0 % age 1.800 s # hottest
total size: 62.000 GiB
The list shows not seemingly hot regions, and only minimum access pattern
diversity. Every region has zero access frequency. The number of region is
10, which is the default ``min_nr_regions value``. Size of each region is also
nearly identical. We can suspect this is because “adaptive regions adjustment”
mechanism was not well working. As the guide suggested, we can get relative
hotness of regions using ``age`` as the recency information. That would be
better than nothing, but given the fact that the longest age is only about 6
seconds while we waited about ten minutes, it is unclear how useful this will
be.
The temperature ranges to total size of regions of each range histogram
visualization of the results also shows no interesting distribution pattern. ::
# damo report access --style temperature-sz-hist
<temperature> <total size>
[-,590,000,000, -,549,000,000) 5.985 GiB |********** |
[-,549,000,000, -,508,000,000) 12.074 GiB |********************|
[-,508,000,000, -,467,000,000) 0 B | |
[-,467,000,000, -,426,000,000) 12.052 GiB |********************|
[-,426,000,000, -,385,000,000) 0 B | |
[-,385,000,000, -,344,000,000) 3.992 GiB |******* |
[-,344,000,000, -,303,000,000) 5.213 GiB |********* |
[-,303,000,000, -,262,000,000) 12.109 GiB |********************|
[-,262,000,000, -,221,000,000) 5.282 GiB |********* |
[-,221,000,000, -,180,000,000) 0 B | |
[-,180,000,000, -,139,000,000) 5.293 GiB |********* |
total size: 62.000 GiB
In short, the parameters provide poor quality monitoring results for hot
regions detection. According to the :ref:`guide
<damon_design_monitoring_params_tuning_guide>`, this is due to the too short
aggregation interval.
100ms/2s intervals: Starts Showing Small Hot Regions
====================================================
Following the guide, increase the interval 20 times (100 milliseocnds and 2
seconds for sampling and aggregation intervals, respectively). ::
# damo start -s 100ms -a 2s
# sleep 600
# damo record --snapshot 0 1
# damo stop
# damo report access --sort_regions_by temperature
0 addr 10.180 GiB size 6.117 GiB access 0 % age 7 m 8 s # coldest
1 addr 49.275 GiB size 6.195 GiB access 0 % age 6 m 14 s
2 addr 62.421 GiB size 3.579 GiB access 0 % age 6 m 4 s
3 addr 40.154 GiB size 6.127 GiB access 0 % age 5 m 40 s
4 addr 16.296 GiB size 6.182 GiB access 0 % age 5 m 32 s
5 addr 34.254 GiB size 5.899 GiB access 0 % age 5 m 24 s
6 addr 46.281 GiB size 2.995 GiB access 0 % age 5 m 20 s
7 addr 28.420 GiB size 5.835 GiB access 0 % age 5 m 6 s
8 addr 4.000 GiB size 6.180 GiB access 0 % age 4 m 16 s
9 addr 22.478 GiB size 5.942 GiB access 0 % age 3 m 58 s
10 addr 55.470 GiB size 915.645 MiB access 0 % age 3 m 6 s
11 addr 56.364 GiB size 6.056 GiB access 0 % age 2 m 8 s
12 addr 56.364 GiB size 4.000 KiB access 95 % age 16 s
13 addr 49.275 GiB size 4.000 KiB access 100 % age 8 m 24 s # hottest
total size: 62.000 GiB
# damo report access --style temperature-sz-hist
<temperature> <total size>
[-42,800,000,000, -33,479,999,000) 22.018 GiB |***************** |
[-33,479,999,000, -24,159,998,000) 27.090 GiB |********************|
[-24,159,998,000, -14,839,997,000) 6.836 GiB |****** |
[-14,839,997,000, -5,519,996,000) 6.056 GiB |***** |
[-5,519,996,000, 3,800,005,000) 4.000 KiB |* |
[3,800,005,000, 13,120,006,000) 0 B | |
[13,120,006,000, 22,440,007,000) 0 B | |
[22,440,007,000, 31,760,008,000) 0 B | |
[31,760,008,000, 41,080,009,000) 0 B | |
[41,080,009,000, 50,400,010,000) 0 B | |
[50,400,010,000, 59,720,011,000) 4.000 KiB |* |
total size: 62.000 GiB
DAMON found two distinct 4 KiB regions that pretty hot. The regions are also
well aged. The hottest 4 KiB region was keeping the access frequency for about
8 minutes, and the coldest region was keeping no access for about 7 minutes.
The distribution on the histogram also looks like having a pattern.
Especially, the finding of the 4 KiB regions among the 62 GiB total memory
shows DAMON’s adaptive regions adjustment is working as designed.
Still the number of regions is close to the ``min_nr_regions``, and sizes of
cold regions are similar, though. Apparently it is improved, but it still has
rooms to improve.
400ms/8s intervals: Pretty Improved Results
===========================================
Increase the intervals four times (400 milliseconds and 8 seconds
for sampling and aggregation intervals, respectively). ::
# damo start -s 400ms -a 8s
# sleep 600
# damo record --snapshot 0 1
# damo stop
# damo report access --sort_regions_by temperature
0 addr 64.492 GiB size 1.508 GiB access 0 % age 6 m 48 s # coldest
1 addr 21.749 GiB size 5.674 GiB access 0 % age 6 m 8 s
2 addr 27.422 GiB size 5.801 GiB access 0 % age 6 m
3 addr 49.431 GiB size 8.675 GiB access 0 % age 5 m 28 s
4 addr 33.223 GiB size 5.645 GiB access 0 % age 5 m 12 s
5 addr 58.321 GiB size 6.170 GiB access 0 % age 5 m 4 s
[...]
25 addr 6.615 GiB size 297.531 MiB access 15 % age 0 ns
26 addr 9.513 GiB size 12.000 KiB access 20 % age 0 ns
27 addr 9.511 GiB size 108.000 KiB access 25 % age 0 ns
28 addr 9.513 GiB size 20.000 KiB access 25 % age 0 ns
29 addr 9.511 GiB size 12.000 KiB access 30 % age 0 ns
30 addr 9.520 GiB size 4.000 KiB access 40 % age 0 ns
[...]
41 addr 9.520 GiB size 4.000 KiB access 80 % age 56 s
42 addr 9.511 GiB size 12.000 KiB access 100 % age 6 m 16 s
43 addr 58.321 GiB size 4.000 KiB access 100 % age 6 m 24 s
44 addr 9.512 GiB size 4.000 KiB access 100 % age 6 m 48 s
45 addr 58.106 GiB size 4.000 KiB access 100 % age 6 m 48 s # hottest
total size: 62.000 GiB
# damo report access --style temperature-sz-hist
<temperature> <total size>
[-40,800,000,000, -32,639,999,000) 21.657 GiB |********************|
[-32,639,999,000, -24,479,998,000) 17.938 GiB |***************** |
[-24,479,998,000, -16,319,997,000) 16.885 GiB |**************** |
[-16,319,997,000, -8,159,996,000) 586.879 MiB |* |
[-8,159,996,000, 5,000) 4.946 GiB |***** |
[5,000, 8,160,006,000) 260.000 KiB |* |
[8,160,006,000, 16,320,007,000) 0 B | |
[16,320,007,000, 24,480,008,000) 0 B | |
[24,480,008,000, 32,640,009,000) 0 B | |
[32,640,009,000, 40,800,010,000) 16.000 KiB |* |
[40,800,010,000, 48,960,011,000) 8.000 KiB |* |
total size: 62.000 GiB
The number of regions having different access patterns has significantly
increased. Size of each region is also more varied. Total size of non-zero
access frequency regions is also significantly increased. Maybe this is already
good enough to make some meaningful memory management efficiency changes.
800ms/16s intervals: Another bias
=================================
Further double the intervals (800 milliseconds and 16 seconds for sampling
and aggregation intervals, respectively). The results is more improved for the
hot regions detection, but starts looking degrading cold regions detection. ::
# damo start -s 800ms -a 16s
# sleep 600
# damo record --snapshot 0 1
# damo stop
# damo report access --sort_regions_by temperature
0 addr 64.781 GiB size 1.219 GiB access 0 % age 4 m 48 s
1 addr 24.505 GiB size 2.475 GiB access 0 % age 4 m 16 s
2 addr 26.980 GiB size 504.273 MiB access 0 % age 4 m
3 addr 29.443 GiB size 2.462 GiB access 0 % age 4 m
4 addr 37.264 GiB size 5.645 GiB access 0 % age 4 m
5 addr 31.905 GiB size 5.359 GiB access 0 % age 3 m 44 s
[...]
20 addr 8.711 GiB size 40.000 KiB access 5 % age 2 m 40 s
21 addr 27.473 GiB size 1.970 GiB access 5 % age 4 m
22 addr 48.185 GiB size 4.625 GiB access 5 % age 4 m
23 addr 47.304 GiB size 902.117 MiB access 10 % age 4 m
24 addr 8.711 GiB size 4.000 KiB access 100 % age 4 m
25 addr 20.793 GiB size 3.713 GiB access 5 % age 4 m 16 s
26 addr 8.773 GiB size 4.000 KiB access 100 % age 4 m 16 s
total size: 62.000 GiB
# damo report access --style temperature-sz-hist
<temperature> <total size>
[-28,800,000,000, -23,359,999,000) 12.294 GiB |***************** |
[-23,359,999,000, -17,919,998,000) 9.753 GiB |************* |
[-17,919,998,000, -12,479,997,000) 15.131 GiB |********************|
[-12,479,997,000, -7,039,996,000) 0 B | |
[-7,039,996,000, -1,599,995,000) 7.506 GiB |********** |
[-1,599,995,000, 3,840,006,000) 6.127 GiB |********* |
[3,840,006,000, 9,280,007,000) 0 B | |
[9,280,007,000, 14,720,008,000) 136.000 KiB |* |
[14,720,008,000, 20,160,009,000) 40.000 KiB |* |
[20,160,009,000, 25,600,010,000) 11.188 GiB |*************** |
[25,600,010,000, 31,040,011,000) 4.000 KiB |* |
total size: 62.000 GiB
It found more non-zero access frequency regions. The number of regions is still
much higher than the ``min_nr_regions``, but it is reduced from that of the
previous setup. And apparently the distribution seems bit biased to hot
regions.
Conclusion
==========
With the above experimental tuning results, we can conclude the theory and the
guide makes sense to at least this workload, and could be applied to similar
cases.
|
