How our Public Cloud instances benefit from NVMe architecture

Every company is seeing its volume of data growing at an exponential rate, and it seems there is no way to reduce the quantity of data we rely on everyday. But at the same time, we have to extract value from this data, in order to optimise, improve and accelerate our business and the way we work. To do so, a large quantity of data must be stored, computed and enhanced, for which specific solutions are needed. In concrete terms, large databases, distributed databases, big data clusters and other resource-intensive workloads require servers with high-performance storage devices, designed to deliver read/write operations at optimal speeds.

At OVHcloud, we love pragmatic solutions. In that spirit, some months ago, we started to offer GPUs in our Public Cloud, i.e. providing virtual machines with GPUs. But GPU virtualisation is not currently able to offer the level of performance we demand, so we chose to link the GPUs directly to the virtual machines, avoiding the virtualisation layer. KVM – our Public Cloud’s hypervisor – uses libvirt, which has a PCI passthrough feature that turned out to be exactly what we needed for this purpose.

NVMe architecture for our Public Cloud instances

In order to provide the best storage performance, we worked with a number of our customers on a PoC that used the same PCI Passthrough feature to incorporate the fastest storage device into our Public Cloud instances: NVMe cards with 1.8TB of space.

When it comes to storage and customer data, we have to be sure that when a customer deletes and releases an instance, we properly clean the device before allocating it to another instance. In this case, we patched OpenStack Nova in order to conduct a full erase of the device. In a nutshell, when an IOPS instance is released by a customer, it’s pushed to quarantine, where internal tools will run the required erase actions on the device. Once it’s done and checked, the device and the instance slot are pushed back in Nova as “available”.

You say fast, but how fast?

Let’s jump into some concrete examples and take the time to appreciate the awesome speed of these new instances! We’ll use the biggest instance model and run an I/O bench on a RAID 0. This way, we will see what the limits are when we aim for the fastest storage solution on a simple Public Cloud instance.

First, create an i1-180 instance, using the OpenStack CLI.

$ openstack server create --flavor i1-180 --image "Ubuntu 19.04" \
  --net Ext-Net --key-name mykey db01

Check the NVMe devices on the instance.

$ lsblk | grep nvme
nvme2n1 259:0    0  1.8T  0 disk
nvme1n1 259:1    0  1.8T  0 disk
nvme0n1 259:2    0  1.8T  0 disk
nvme3n1 259:3    0  1.8T  0 disk

We have our four NVMe devices, so let’s create a RAID 0 with them.

$ mdadm --create /dev/md1 --level 0 --raid-devices 4 \
  /dev/nvme0n1 /dev/nvme1n1 /dev/nvme2n1 /dev/nvme3n1
mdadm: Defaulting to version 1.2 metadata
mdadm: array /dev/md1 started

Now we’ll format the raid device.

$ mkfs.xfs /dev/md1
meta-data=/dev/md1               isize=512    agcount=32, agsize=58601344 blks
         =                       sectsz=512   attr=2, projid32bit=1
         =                       crc=1        finobt=1, sparse=0, rmapbt=0, reflink=0
data     =                       bsize=4096   blocks=1875243008, imaxpct=5
         =                       sunit=128    swidth=512 blks
naming   =version 2              bsize=4096   ascii-ci=0 ftype=1
log      =internal log           bsize=4096   blocks=521728, version=2
         =                       sectsz=512   sunit=8 blks, lazy-count=1
realtime =none                   extsz=4096   blocks=0, rtextents=0

After mounting the file system on /mnt, we are ready to run the test.

Read test

We’ll start with the read test, using 4k blocks, and as we have 32 vCores on this model, we’ll use 32 jobs. Let’s GO!

$ fio --bs=4k --direct=1 --rw=randread --randrepeat=0 \
  --ioengine=libaio --iodepth=32 --runtime=120 --group_reporting \
  --time_based --filesize=64m --numjobs=32 --name=/mnt/test
/mnt/test: (g=0): rw=randread, bs=(R) 4096B-4096B, (W) 4096B-4096B, (T) 4096B-4096B, ioengine=libaio, iodepth=32
[...]
fio-3.12
Starting 32 processes
Jobs: 32 (f=32): [r(32)][100.0%][r=9238MiB/s][r=2365k IOPS][eta 00m:00s]
/mnt/test: (groupid=0, jobs=32): err= 0: pid=3207: Fri Nov 29 16:00:13 2019
  read: IOPS=2374k, BW=9275MiB/s (9725MB/s)(1087GiB/120002msec)
    slat (usec): min=2, max=16031, avg= 7.39, stdev= 4.90
    clat (usec): min=27, max=16923, avg=419.32, stdev=123.28
     lat (usec): min=31, max=16929, avg=427.64, stdev=124.04
    clat percentiles (usec):
     |  1.00th=[  184],  5.00th=[  233], 10.00th=[  269], 20.00th=[  326],
     | 30.00th=[  363], 40.00th=[  388], 50.00th=[  412], 60.00th=[  437],
     | 70.00th=[  465], 80.00th=[  506], 90.00th=[  570], 95.00th=[  635],
     | 99.00th=[  775], 99.50th=[  832], 99.90th=[  971], 99.95th=[ 1037],
     | 99.99th=[ 1205]
   bw (  KiB/s): min=144568, max=397648, per=3.12%, avg=296776.28, stdev=46580.32, samples=7660
   iops        : min=36142, max=99412, avg=74194.06, stdev=11645.08, samples=7660
  lat (usec)   : 50=0.01%, 100=0.02%, 250=7.41%, 500=71.69%, 750=19.59%
  lat (usec)   : 1000=1.22%
  lat (msec)   : 2=0.07%, 4=0.01%, 10=0.01%, 20=0.01%
  cpu          : usr=37.12%, sys=62.66%, ctx=207950, majf=0, minf=1300
  IO depths    : 1=0.1%, 2=0.1%, 4=0.1%, 8=0.1%, 16=0.1%, 32=100.0%, >=64=0.0%
     submit    : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     complete  : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.1%, 64=0.0%, >=64=0.0%
     issued rwts: total=284924843,0,0,0 short=0,0,0,0 dropped=0,0,0,0
     latency   : target=0, window=0, percentile=100.00%, depth=32
 
Run status group 0 (all jobs):
   READ: bw=9275MiB/s (9725MB/s), 9275MiB/s-9275MiB/s (9725MB/s-9725MB/s), io=1087GiB (1167GB), run=120002-120002msec
 
Disk stats (read/write):
    md1: ios=284595182/7, merge=0/0, ticks=0/0, in_queue=0, util=0.00%, aggrios=71231210/1, aggrmerge=0/0, aggrticks=14348879/0, aggrin_queue=120, aggrutil=99.95%
  nvme0n1: ios=71231303/2, merge=0/0, ticks=14260383/0, in_queue=144, util=99.95%
  nvme3n1: ios=71231349/0, merge=0/0, ticks=14361428/0, in_queue=76, util=99.89%
  nvme2n1: ios=71231095/0, merge=0/0, ticks=14504766/0, in_queue=152, util=99.95%
  nvme1n1: ios=71231096/4, merge=0/1, ticks=14268942/0, in_queue=108, util=99.93%

2,370K IOPS. Those are awesome figures, aren’t they?

Write test

Ready for the write test?

$ fio --bs=4k --direct=1 --rw=randwrite --randrepeat=0 --ioengine=libaio --iodepth=32 --runtime=120 --group_reporting --time_based --filesize=64m --numjobs=32 --name=/mnt/test
/mnt/test: (g=0): rw=randwrite, bs=(R) 4096B-4096B, (W) 4096B-4096B, (T) 4096B-4096B, ioengine=libaio, iodepth=32
[...]
fio-3.12
Starting 32 processes
Jobs: 32 (f=32): [w(32)][100.0%][w=6702MiB/s][w=1716k IOPS][eta 00m:00s]
/mnt/test: (groupid=0, jobs=32): err= 0: pid=3135: Fri Nov 29 15:55:10 2019
  write: IOPS=1710k, BW=6680MiB/s (7004MB/s)(783GiB/120003msec); 0 zone resets
    slat (usec): min=2, max=14920, avg= 6.88, stdev= 6.20
    clat (nsec): min=1152, max=18920k, avg=587644.99, stdev=735945.00
     lat (usec): min=14, max=18955, avg=595.46, stdev=736.00
    clat percentiles (usec):
     |  1.00th=[   21],  5.00th=[   33], 10.00th=[   46], 20.00th=[   74],
     | 30.00th=[  113], 40.00th=[  172], 50.00th=[  255], 60.00th=[  375],
     | 70.00th=[  644], 80.00th=[ 1139], 90.00th=[ 1663], 95.00th=[ 1991],
     | 99.00th=[ 3490], 99.50th=[ 3949], 99.90th=[ 4686], 99.95th=[ 5276],
     | 99.99th=[ 6521]
   bw (  KiB/s): min=97248, max=252248, per=3.12%, avg=213714.71, stdev=32395.61, samples=7680
   iops        : min=24312, max=63062, avg=53428.65, stdev=8098.90, samples=7680
  lat (usec)   : 2=0.01%, 4=0.01%, 10=0.01%, 20=0.86%, 50=11.08%
  lat (usec)   : 100=15.35%, 250=22.16%, 500=16.34%, 750=6.69%, 1000=5.03%
  lat (msec)   : 2=17.66%, 4=4.38%, 10=0.44%, 20=0.01%
  cpu          : usr=20.40%, sys=41.05%, ctx=113183267, majf=0, minf=463
  IO depths    : 1=0.1%, 2=0.1%, 4=0.1%, 8=0.1%, 16=0.1%, 32=100.0%, >=64=0.0%
     submit    : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     complete  : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.1%, 64=0.0%, >=64=0.0%
     issued rwts: total=0,205207842,0,0 short=0,0,0,0 dropped=0,0,0,0
     latency   : target=0, window=0, percentile=100.00%, depth=32
 
Run status group 0 (all jobs):
  WRITE: bw=6680MiB/s (7004MB/s), 6680MiB/s-6680MiB/s (7004MB/s-7004MB/s), io=783GiB (841GB), run=120003-120003msec
 
Disk stats (read/write):
    md1: ios=0/204947351, merge=0/0, ticks=0/0, in_queue=0, util=0.00%, aggrios=0/51301962, aggrmerge=0/0, aggrticks=0/27227774, aggrin_queue=822252, aggrutil=100.00%
  nvme0n1: ios=0/51302106, merge=0/0, ticks=0/29636384, in_queue=865064, util=100.00%
  nvme3n1: ios=0/51301711, merge=0/0, ticks=0/25214532, in_queue=932708, util=100.00%
  nvme2n1: ios=0/51301636, merge=0/0, ticks=0/34347884, in_queue=1089896, util=100.00%
  nvme1n1: ios=0/51302396, merge=0/0, ticks=0/19712296, in_queue=401340, util=100.00%

1,710K IOPS on the write operation… Imagine what you could do with such a solution for your databases, or other highly-intensive, transactional use cases.

Of course, we’re presenting an optimal scenario for this example. RAID 0 is inherently risky, so any failure on one of the NVMe devices can corrupt your data. This means you absolutely must create backups for your critical data, but this in itself opens up a lot of new possibilities. So we’re 100% sure that your databases will love these instances! You can find more details about them on our Public Cloud website.