Diagnostics and analysis of disk subsystem performance

In this article, we will review one of the cases that our clients ask for technical support. The point is as follows: the server runs specific software, for example, for calculating data cubes, heavily working with the database. At some point, the productivity of the software decreases. The client believes that the problem in the disk subsystem of the server and contacts Cloud4Y technical support for the appropriate diagnosis.

The test starts on our side: engineers analyze the overall load, including the storage and utilization of the resources allocated to it in the ticket of the VM. When it is clear that there are no problems "outside", they start diagnosing "inside" the VMs. This is what we will explain further on.

The logic of the method is quite simple: run a couple of rounds of measurements. The 1st with real workload, the 2nd - with and without test load. This will allow, firstly, to get the iops benchmarks at maximum load of the disk subsystem with tests and at idle. And secondly, compare them with the obtained performance metrics at real workload.

In order to demonstrate the method of diagnosis, we deployed a test in the form of a virtual machine based on Ubuntu 20.04 x64, 2 CPU cores, 4 GB memory, 30 GB disk with a vcd-type-med profile - a limit of 1000 iops.

The industry standard for checking iops on *nix systems is the iostat utility from the SYSSTAT package, and load testing is done with the fio utility - they must be installed:

sudo apt update
sudo apt install sysstat
sudo apt install fio

After installation, run the first round of measurements under real load for at least 10 minutes, for example:

iostat -x -t -o JSON 10 60 > "iostat-1.json" 


The iostat parameters mean to take 60 measurements with an interval every 10 seconds, the output in json-format is sent to the file iostat-<#_round>.json. You can set the number and the intervals on your own - in one case we took measurements throughout the day with an hourly breakdown.

As for the json-format, it is much easier to process it later using software than parsing the utility output with regexp, but it is only supported in fairly recent versions - 12.x - and if you have a very old guest OS, such as Ubuntu 16.04 or lower, you will most likely have to build the package from source.

In the second round, turn off the running load and turn on the measurements

iostat -x -t -o JSON 10 60 > "iostat-2.json"

and several times within 10 minutes of iostat running, turn on the test load with fio for 1-2 minutes:

fio --rate_iops=700,300, --bs=4k --rw=randrw --percentage_random=50 --rwmixread=70 --rwmixwrite=30 --direct=1 --iodepth=256 --time_based --group_reporting --name=iops-test-job --numjobs=1 --filename=runfio.sh.test --size=1GB --ioengine=libaio --runtime=60 --eta-newline=10

The parameter --rate_iops=700,300 is specified in the format --rate_iops=[read],[write],[trim] and means "give 700 iops for read, 300 for write, leave trim as default". The numbers are chosen based on the total iops limit specified in Cloud Director for a particular disk and load distribution of 70% read / 30% write. In the case of the testing stand, the total limit is 1000 iops, so the load is distributed as 700/300.

An important nuance: if you do not limit the load, fio will generate as many IOs as possible, which will cause the latency of virtual disk IOs to increase many times in the test due to vSphere's throttling of "unnecessary" operations: The VM will constantly try to do iops more than it is supposed to, while vSphere will adjust the queue depth so as to hold on to the "extra" operations before sending them to the storage in order to comply with the limit.

Both tests confirm that the disk will work for 1000 IO operations, but in the first case, without --rate_iops=W,R, we will get throttling latency and in the second we will get real values.


For latency checking you can use a separate utility, ioping:

# installation 
$  sudo apt-get install ioping

# check 
$ ioping -c 9 /tmp/
4 KiB <<< /tmp/ (ext4 /dev/sda5): request=1 time=309.1 us (warmup)
4 KiB <<< /tmp/ (ext4 /dev/sda5): request=2 time=717.3 us
4 KiB <<< /tmp/ (ext4 /dev/sda5): request=3 time=583.4 us
4 KiB <<< /tmp/ (ext4 /dev/sda5): request=4 time=430.2 us
4 KiB <<< /tmp/ (ext4 /dev/sda5): request=5 time=405.8 us
4 KiB <<< /tmp/ (ext4 /dev/sda5): request=6 time=387.4 us
4 KiB <<< /tmp/ (ext4 /dev/sda5): request=7 time=382.1 us (fast)
4 KiB <<< /tmp/ (ext4 /dev/sda5): request=8 time=811.6 us (slow)
4 KiB <<< /tmp/ (ext4 /dev/sda5): request=9 time=706.9 us

--- /tmp/ (ext4 /dev/sda5) ioping statistics ---
8 requests completed in 4.42 ms, 32 KiB read, 1.81 k iops, 7.06 MiB/s
generated 9 requests in 8.00 s, 36 KiB, 1 iops, 4.50 KiB/s
min/avg/max/mdev = 382.1 us / 553.1 us / 811.6 us / 162.7 us

Additionally, we recommend capturing screenshots of the top / htop, especially for the 1st round. The LA (Load Average) information can help to find possible reasons for slow software performance. Also, in case of multiple disks or LVM, the commands lsblk -f/ls -l /dev/mapper will visualize the structure of your disk subsystem.

The processing and visualization of the json-files of measurements was done in Python in the JupyterLab environment, using the pandas and matplotlib libraries. You can use our variant or write your own - we have prepared for you an archive with sources and an example of processing measurements in the form of - ipynb - jupiter-notepad.

In the end, according to the graphs you can either definitely exclude problems with the disk subsystem, or optimize it, or reasonably change the disk policy to a more productive one.

Example of test stand graphs. Test load on the graphs: 700 iops per read and 300 per write. For obvious reasons, there are no workload graphs.


And below are the graphs taken from a real client's ticket diagnostic case. There was no need to stop operation to switch on test load: the graphs clearly show peaks of 40 iops per write and 200 iops per read, i.e. problems with disk subsystem were not detected, disks were not almost utilized, and low performance is caused by absolutely different reason.


Above, we advised you to pay attention to the Load Average indicator and take screenshots of the utility top or htop. The meaning of this indicator, converted to one processor core, is as follows:


  • la < 1 - the system still has free computing resources, there is no process queue
  • la = 1 - no resources, no queue at the moment
  • la > 1 - there is a queue where processes are waiting for CPU resources to be released for their execution

Keep this value between 0.7 and 0.8: on the one hand there is still some free resources available, and on the other hand the CPU utilization is rather good. In this case la = 80 / 32 = 2.5.
This was the reason for low software performance: the queue of processes waiting for execution was 1.5 times longer than the one already executed on the CPU -> the load was 250% -> the software was "slow".


List of useful materials

briefly about iops limit trolling https://communities.vmware.com/t5/vSphere-Hypervisor-Discussions/After-apply-the-DISK-IOPS-limit-my-storage-latency-increased/m-p/1344871/highlight/true#M2945 

VMWare article on limiting mechanisms https://core.vmware.com/blog/performance-metrics-when-using-iops-limits-vsan-what-you-need-know 

VMWare official documentation https://core.vmware.com/resource/vsan-operations-guide#sec6732-sub5 

why limits are necessary https://www.vmgu.ru/news/vmware-vsphere-virtual-disk-vmdk-iops-limit 

Presentation on mClock algorithm - hypervisor IO scheduler, including storage management https://www.usenix.org/legacy/events/osdi10/tech/slides/gulati.pdf 

fio official documentation https://fio.readthedocs.io/en/latest/fio_man.html#fio-manpage 

official iostat documentation http://sebastien.godard.pagesperso-orange.fr/man_iostat.html 

  • iops, Python, JupiterLab, MatPlotLib, Pandas, SYSSTAT, iostat, fio, ioping
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