Lord Kelvin Vs the IO blender

One of the characteristics of a  successful storage system for virtualized environments is that it must handle the IO blender.  Put simply, when lots of regular looking workloads are virtualized and presented to the storage, their regularity is lost, and the resulting IO stream starts to look more and more random.

 This is very similar to the way that synthesisers work – they take multiple regular sine waves of varying frequencies and add them together to get a much more complex sound.

 http://msp.ucsd.edu/techniques/v0.11/book-html/node14.html#fig01.08

That’s all pretty awesome for making cool space noises, but not so much when presented to the storage OS.  Without the ability to detect regularity, things like caching, pre-fetching and any kind of predictive algorithm break down.

That pre-fetch is never going to happen.

In Nutanix NOS we treat each of these sine waves (workloads) individually, never letting them get mixed together.  NDFS knows about vmdk’s or vhdx disks – and so by keeping the regular workloads separate we can still apply all the usual techniques to keep the bits flowing, even at high loads and disparate workload mixes that cause normal storage systems to fall over in a steaming heap of cache misses and metadata chaos.

 

Multiple devices/jobs in fio

If your underlying filesystem/devices have different response times (e.g. some devices are cached – or are on SSD) and others are on spinning disk, then the behavior of fio can be quite different depending on how the fio config file is specified.  Typically there are two approaches

1) Have a single “job” that has multiple devices

2) Make each device a “job”

With a single job, the iodepth parameter will be the total iodepth for the job (not per device) .  If multiple jobs are used (with one device per job) then the iodepth value is per device.

Option 1 (a single job) results in [roughly] equal IO across disks regardless of response time.  This is like  having a volume manager or RAID device, in that the overall oprate is limited by the slowest device.

For example, notice that even though the wait/response times are quite uneven (ranging from 0.8 ms to 1.5ms) the r/s rate is quite even.  You will notice though the that queue size is very variable so as to achieve similar throughput in the face of uneven response times.

Screen Shot 2014-06-17 at 5.34.55 PM

To get this sort of behavior use the following fio syntax. We allow fio to use up to 128 outstanding IO’s to distribute amongst the 8 “files” or in this case “devices”. In order to maintain the maximum throughput for the overall job, the devices with slower response times have more outstanding IO’s than the devices with faster response times.

The second option, give an uneven throughput because each device is linked to a separate job, and so is completely independent.  The  iodepth parameter is specific to each device, so every device has 16 outstanding IO’s.  The throughput (r/s) is directly tied to the response time of the specific device that it is working on.  So response times that are 10x faster generate throughput that is 10x faster.  For simulating real workloads this is probably not what you want.

For instance when sizing workingset and cache, the disks that have better throughput may dominate the cache.

Screen Shot 2014-06-17 at 5.32.52 PM

Here be Zeroes

zero-cup

Many storage devices/filesystems treat blocks containing nothing but zeros in a special way, often short-circuiting reads from the back-end.  This is normally a good thing but this behavior can cause odd results when benchmarking.  This typically comes up when testing against storage using raw devices that have been thin provisioned.

In this example, I have several disks attached to my linux virtual machine.  Some of these disks contain data, but some of them have never been written to.

When I run an fio test against the disks, we can clearly see that the response time is better for some than for others.  Here’s the fio output…

fio believes that it is issuing the same workload to all disks.
fio believes that it is issuing the same workload to all disks.

and here is the output of iostat -x

Screen Shot 2014-06-12 at 4.29.23 PM

The devices sdf, sdg and sdh are thin provisioned disks that have never been written to. The read response times are be much lower.  Even though the actual storage is identical.

There are a few ways to detect that the data being read is all zero’s.

Firstly use a simple tool like unix “od” or “hd”  to dump out a small section of the disk device and see what it contains.  In the example below I just take the first 1000 bytes and check to see if there is any data.

Screen Shot 2014-06-12 at 4.58.15 PM

Secondly, see if your storage/filesystem has a way to show that it read zeros from the device.  NDFS has a couple of ways of doing that.  The easiest is to look at the 2009:/latency page and look for the stage “FoundZeroes”.

 

If your storage is returning zeros and so making your benchmarking problematic, you will need to get some data onto the disks!  Normally I just do a large sequential write with whatever benchmarking tool that I am using.  Both IOmeter and fio will write “junk” to disks when writing.