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Posts Tagged ‘disk’

This is the third in a multi-part series on storage basics.  I’ve had some good feedback from folks in the SMB space saying that the first couple posts in this series have been beneficial, so we’ll be sticking with some basic concepts for another post or two before we dive into some nitty-gritty details and practical applications of these concepts in a VMware environment.  In the second post of this series I introduced the concept of IOPS and explained how the physical characteristics of a hard disk drive determine the theoretical IOPS capability of a disk.  I then noted that you can aggregate disks to achieve a greater number of IOPS for a particular storage environment.  Today, we will look at just how you combine multiple disks and the performance impact of doing so.  Remember that we are keeping this simple; the concepts I present here may not apply to that fancy new SAN you just purchased with your end-of-year money or the cheap little SATA controller on your desktop’s motherboard (not that there’s anything wrong with it) – we’re more in the middle ground of direct attached storage (DAS) as we firm up concepts.

Enterprise servers and storage systems have the ability to combine multiple disks into a group using Redundant Array of Independent Disks (RAID) technology.  We’ll assume a hardware RAID controller is responsible for configuring and driving storage IO to the connected disks.  RAID controllers typically have battery-backed cache (we’ll talk cache in a future post), an interconnect where the drives plug in, such as SCSI or SAS (we’ll talk about these too in a future post), and hold the configuration of the RAID set including stripe size and RAID level.  The controller also does the basic work of reading and writing on RAID set – mirroring, striping, and parity calculations.  There are several different types of RAID level – rather than rehash the details of them, read the Wikipedia entry on RAID and then come back here….

Ok, great.  So you now know that RAID is implemented to increase performance through the aggregation of multiple disks, and to increase reliability though mirroring and parity.  Now let’s consider the performance implications of some basic RAID levels.  As with many things in the IT industry, there are trade-offs: security vs. usability, brains vs. brawn, and now performance vs. reliability.  As we increase reliability in a RAID array through mirroring and parity, performance can be impacted.  This is where the more disks = more IOPS bit starts to fall apart.  The exact impact depends on the RAID type.  Here are some examples of how RAID impact the maximum theoretical IOPS using the most common RAID levels, where:

I = Total IOPS for Array (note that I show Read and Write separately)

i = IOPS per disk in array (based on spindle speed averages from Part II: IOPS)

n = Number of disks in array

r = Percentage of read IOPS (calculated from the Average Disk Reads/Sec divided by total Average Disk Transfers/Sec in your Windows Perfmon)

w = Percentage of write IOPS (calculated from the Average Disk Writes/Sec divided by total Average Disk Transfers/Sec in your Windows Perfmon)

RAID0 (striping, no redundancy)

This is basic aggregation with no redundancy.  A single drive error/failure could render your data useless and as such it is not recommended for production use.  It does allow for some simple math:

I =n*i

Because there is no mirroring or parity overhead, theoretical maximum Read and Write IOPS are the same.

RAID 1 & RAID10 (mirroring technologies):

Because data is mirrored to multiple disks

Read I = n*i

For example, if we have six 15k disks in a RAID10 config, we should expect a theoretical maximum number of IOPS for our array to be 6*180 = 1080 IOPS

Write I = (n*i)/2

RAID5 (striping with a single parity disk)

Read I = (n-1)*i

Example: Five 15k disks in a RAID 5 (4 + 1) will yield a maximum IOPS of (5-1)*180 = 720 READ IOPS.  We subtract 1 because one of the disks holds parity bits, not data.

Write I = (n*i)/4

Example: Five disks in a RAID 5 (4 + 1) will yield a maximum IOPS of (5*180)/4 = 225 WRITE IOPS

Again, these formulas are very basic and have little practical value.  Furthermore, it is seldom that you will find a system that is doing only reads or only writes.  More often, as is the case with typical VMware environments, reads and writes are mixed.  An understanding of your workload is key to accurately sizing your storage environment for performance.  One of the workload characteristics (we’ll explore some more in the future) that you should consider in your sizing is the percentage of read IOPS vs. the percentage of write IOPS.  A formula like this gets you close if you want to do the math for a mixed read/write environment in a RAID5 set:

I = (n*i)/(r+4 *w)

Example: a 60% read/40% write workload with five 15k disks in a RAID5 would provide (5*180)/(.6+4*.4) = 409 IOPS.

The previous examples have all been from the perspective of the storage system.  If we take a look at this from the server/OS/application side, something interesting shows up.  Let’s say you fired up Windows perfmon and collected Physical Disk Transfers/sec counters every 15 seconds for 24 hours and analyzed the data in Excel to find the 95th Percentile for total average IOPS (this is a pretty standard exercise if you are buying enterprise storage array or SAN).  Let’s say that you find that the server in question was asking for 1000 IOPS at the 95th Percentile (let’s stick with our 60% read/40% write workload).  And finally, let’s say we put this workload on a RAID5 array.  That’s saying a lot of stuff, but what does it all mean?  Because RAID5 has a write penalty factor of 4 (again, Duncan Epping’s posted a great article here which I referenced in Part II that describes this in a slightly different way) we can tweak the previous formula to show the IO’s to the backend array given a specific workload.

I = Target workload IOPS

f = IO penalty

r = % Read

w = % Write

IO = (I * r) + (I * w) * f

Our example then looks like this (remember work inside parenthesis first, and then My Dear Aunt Sally):

(1000 * .6) + ((1000 * .4) * 4) = 2200

Simply stated, this means that for every 1000 IOPS that our workload requests from our storage system, the backing array perform 2200 IO’s, and it better do it quickly or you will start to see latency and queuing (we call this performance degradation, boys and girls!).  Again, this is a very simplistic approach neglecting factors like cache, randomness of the workload, stripe size, IO size, and partition alignment which can all impact requirements on the backend.  I’ll cover some of those later.

As you can hopefully see, the laws of physics combined with some simple math can provide some pretty useful numbers.  A basic understanding of your array configuration against your workload requirements can go a long way in preventing storage bottlenecks.  You may also find that as you consider the cost per disk against various spindle speeds, capacities and RAID levels that you are better off buying smaller, faster, fewer, more, slower…. disks depending on your requirements.  The geekier amongst us could even take these formulas and some costs per disk and hit up Excel Goal Seek to find the optimal level, but that’s more than this little blog can do for you today.

Before I wrap up this post, I want to leave you with a few more links that I have bookmarked around the topics of IOPS and RAID over the past several years:

• DB sizing for Microsoft Operations Manger, includes a nice chart with formulas similar to the ones I provided in this article: http://blogs.technet.com/jonathanalmquist/archive/2009/04/06/how-can-i-gauge-operations-manager-database-performance.aspx
• An Experts Exchange post with some good info in the last entry on the page (subscription required) http://www.experts-exchange.com/Storage/Storage_Technology/Q_22669077.html
• A Microsoft TechNet article with storage sizing for Exchange – a bit dated but still applicable: http://technet.microsoft.com/en-us/library/aa997052(EXCHG.65).aspx
• A simple whitepaper from Dell on their MD1000 DAS array – easy language to help the less technical along: http://support.dell.com/support/edocs/systems/md1120/multlang/whitepaper/SAS%20MD1xxx.pdf
• A great post that uses some math to show performance and cost trade-offs of RAID level, disk type, and spindle speed.  http://www.yonahruss.com/architecture/raid-10-vs-raid-5-performance-cost-space-and-ha.html
• Another nifty post that looks at cost vs. performance vs capacities of various disks speeds in an array http://blogs.zdnet.com/Ou/?p=322

A reader named Mark contacted me today and asked if there was a way to reduce the size of the batch output from an ESXTOP run.  And he asks for good reason: Depending on the number of VM’s on your host, the delay between ESXTOP samplings and the number of samples you collect, using the All Stats option (-a) can yield a massive file in a short period of time.  If written to a partition on your ESX Service Console you run the risk of filling the partition, and forget about actually being able to analyze the data in PERFMON or Excel.  For example, on an ESX host running ~15 VM’s I produced 100MB worth of CSV using the -a switch, sampling every 15 seconds, for just under 2 hours.  ESXTOP uses 10-second intervals by default; I used -d 15 to change the sampling delay.  Had I went with the default my output would have been bigger.

To reduce the size of your output, you can change your sampling delay to something larger, say 30-seconds.  I suppose you could also capture statistics when the host is not busy so you get fewer characters in the results, but that’s just being goofy.

A better way to reduce your ESXTOP output size is to selectively include only the statistics you are interested in, and is really what Mark was asking.  After all, all statistics from ESXTOP can be too many statistics, and chances are you already know what stats you are interested in.  Here’s how you can narrow down the collected stats for easier analysis and smaller output:

1. Enter ESXTOP in interactive mode on the Service Console by simply typing esxtop at the # prompt
2. Switch to a component you are NOT interested in capturing statistics on by pressing the corresponding menu option (c: ESX cpu, m: ESX memory, d: ESX disk adapter, u: ESX disk device, v: ESX disk VM).
3. Press f when viewing the component you do not want to capture.  A list of fields will be displayed.  You can toggle the fields on and off by pressing the letter corresponding to each field.  An * indicates that the field is on.  You want to turn off all of the fields you don’t want to collect.
4. Repeat steps 2 & 3 for the remaining components, leaving only what you want to capture.
5. Switch to the component you want to capture in batch mode and repeat step #3, except you will now enable what you want to capture.
6. Press W (capital W – case sensitive) to write out the ESXTOP configuration file.  You can accept the default or create new configuration files.  You may want to create a CPU-only config file, memory-only, and so forth.
7. Press CTRL+C to stop ESXTOP.
8. Now, invoke ESXTOP in batch mode, calling your updated or new configuration file you created in step #6 using the -c switch.  Here’s an example:# esxtop -b -d 30 -n 480 -c .esxtopcpustats > /tmp/esxtop_cpu_stats.csv where .esxtopcpustats is an ESXTOP config file with only CPU stats.  -d sets your capture interval to 30 seconds, and -n sets the number of samples to 480 (or 4 hours with a delay of 30 seconds).

Once your capture is complete you can replay the sampling in ESXTOP using replay mode (-R), or you can copy the .csv to a Windows system and use PERFMON or Excel to analyze the stats.  If using PERFMON or Excel you will notice that the system summary information displayed at the top of an interactive ESXTOP session is included in the output (console memory, console cpu, etc.).  As far as I know, there is no way to disable this, nor would you want to as it includes the time stamp necessary to interpret your data.

It is possible to use the vSphere CLI or the vSphere Management Assistant (vMA) to run RESXTOP, a version of ESXTOP designed for remote administration of ESXi or ESX.  You may note, however, RESXTOP from the vSphere CLI only works from a Linux client.  Using either of these tools will help you to automate ESXTOP statistics collection from multiple hosts using customized configuration files.

Here are some bookmarks for resources that I have recently referenced:

• vCenter 4 and ESX 4 Now Use 10 Year Default SSL Certificate | VM /ETC – Rich Brambly has some guidance on installing a new SSL certificate on vCenter, with very useful links in his post to official VMware documentation and KB’s on the subject.
• VMware vSphere Client on Microsoft Windows 7! | Virtual Lifestyle – Heiko Verlande has found a way to run the VMware vSphere Client on Windows 7.
• Virtu-Al » PowerCLI: Daily Report V2 – Version two of a handy PowerShell based VMware Environment Daily Report from VMware vExpert and PowerShell guru Alan Renouf
  What’s new/Bug Fixes
  * Active VMs count
  * Inactive VMs count
  * DRS Migrations count and list
  * Correct NTP Server check for each host
  * VMs stored on local datastores
  * NTP Service check for each host
  * vmkernel warning messages for each host
  * VM CPU ready over x%
• VMware Self-Service- VMware Update Manager Plug-In fails to install -Troubleshooting steps for vCenter Plug-in install problems.
• Using VMware VDI and vmSight for Stronger and Sustainable HIPAA and PCI Compliance – Virtualization brings new options for protecting sensitive data by moving it from the desktop into the datacenter.
• Counter of the Week : Analyzing Storage Performance – The purpose of this article is to provide prescriptive guidance on how to troubleshoot logical and physical disk response times in regards to Windows performance analysis. Start with the following performance counters to analyze disk response…
• NetApp, Compellent, HP, Dell top the field in 12-product test – Network World – A terabyte isn’t what it used to be. Disks are slower than you think. And a Gigabit Ethernet is plenty of bandwidth for many storage applications.