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Posts Tagged ‘SAN’

At the risk of beating a dead horse, it’s time to resurrect my Storage Basics series.  I’ve recently had some great feedback on the series and figured I should round out a few of the concepts before I wrap it up.  I want to cover a topic often discussed amongst virtualization professionals, but one I often find general practitioners and server admins not understanding: storage alignment.  Storage alignment, or the lack of alignment, is not a new issue and is not unique to VMware or virtualization in general.  However, the effects of misaligned storage can be more greatly felt in terms of reduced performance and strain on a storage system in shared, oversubscribed or high I/O environments.  Many others in the virtualization and storage communities have already covered partition alignment (see Duncan Epping, Vaughn Stewart, and most recently Chad Sakac), but I feel it is an important enough topic for me to re-hash as part of this series.

What is Storage Alignment?

Let’s start with a quick overview of what storage alignment means.  Quite simply, storage alignment refers to the positioning (starting offset) of the various pieces of a systems storage components – the physical disk sectors or array’s chunks, the VMware File System (VMFS) in a VMware environment, and the guest file system’s clusters within a partition – in relation to the layer directly under the element in question.  A quick graphic often makes quick work of explaining this (I often whiteboard this concept for colleagues and clients):

As you can see, the starting offset of the VMFS partition does not correspond to the physical segmentation of the underlying disks (in this case, the chunks on a SAN – but could be conceptually replaced with the sectors of a single disk).  Furthermore, the clusters (or blocks) of the guest VM are not aligned to the VMFS partition nor to the underlying storage.  For traditional (physical) systems or VMware RDM’s, the VMFS layer could be abstracted, but the result would be the same – the clusters of a partition would be misaligned to the underlying disk.

What Does it Mean?

Quite simply, misaligned storage (both VMFS partitions and Guest File Systems) can lead to poor performance under certain conditions.  How badly performance is impacted depends on the degree of I/O strain your server and storage are under, the caching mechanisms in your environment, and the architecture of your SAN.  Again, a visual can help explain how misaligned storage can hurt you. For simplicity let’s leave out the VMFS layer as we consider the following diagram (pardon my hasty Visio visualization):

What we see is that the target data in a tiny 16kb read request spans two 64kb chunks on our storage array.  Any reads of that piece of data will result in twice the amount of data as would be minimally necessary being transferred to the host’s storage stack.  The net effect is an increase in the work the storage array must do – gobbling up IOPS that would otherwise be available for the real work of reading data, reducing throughput on the interface, and messing with cache algorithms and dedupe mechanisms on some arrays.  In short, misaligned storage is an efficiency killer.  Now add in the VMFS layer back in and you’ll see how things get complicated.

If (and we’re talking a big IF here) every bit of data you wanted to read spanned a chunk or sector boundary, you could experience half the expected performance due to misalignment.  In reality, depending on your workload and storage technology your performance increase from properly aligning your storage will probably be somewhere between 10-30%.

Want to dig deeper?

There have been some great resources published on this issue over the past few years on storage alignment.  Major vendors have all begun pushing information on the problem – here are some of the best that I have found:

Microsoft has a Knowledge Base article (http://support.microsoft.com/kb/929491) that describes the problem and symptoms of misaligned partitions, how to determine if your partition is aligned, and the use of diskpart to create aligned partitions.

Microsoft also has an in-depth article on MSDN, including some performance numbers at http://msdn.microsoft.com/en-us/library/dd758814.aspx.  Also check out Jimmy May’s series Partition (Sector) alignment for SQL Server here: http://blogs.msdn.com/b/jimmymay/archive/2008/10/14/disk-partition-alignment-for-sql-server-slide-deck.aspx.  One of the best descriptions of the complexities of the problem can be found in Jimmy’s blog series.

VMware has an article here: http://www.vmware.com/pdf/esx3_partition_align.pdf.  Be aware that this article is for Virtual Infrastructure 3, not vSphere 4.0.  Some of the information is now a bit dated.

Netapp has a few documents to check out: http://media.netapp.com/documents/tr-3428.pdf (VI3), and http://media.netapp.com/documents/tr-3749.pdf (vSphere)

EMC covers alignment in their TechBooks for Clariion, Celerra, and Symmetrix.

Tools to Align Partitions:

Ok – so you’ve bought into this whole partition alignment thing as being a real issue.  How to you fix it?  Here are some tools:

1. MSInfo32.exe, wmic, and dmdiag will show you misaligned partitions on Windows machines (check the Microsoft links above for usage info).
2. Diskpart.exe (or diskpar.exe on versions of Windows previous to 2003) creates aligned partitions on Windows systems.  Diskpart cannot be used to realign a previously created partition, only to create new correctly aligned partitions.
3. MBRScan/MBRAlign from NetApp can report on and realign existing virtual disks on a VMware ESX server.  Also a nifty PowerShell script from NetApp to find if your partitions are aligned: http://communities.netapp.com/docs/DOC-6175
4. vOptimizer from Vizioncore can report on and realign existing virtual disks.
5. GParted can be used to create aligned partitions on both Windows and Linux machines, and to realign some existing partitions.
6. VMware vCenter – VMFS datastores created using vCenter are aligned automatically. Note – Guest VMDK’s are not aligned automatically by vCenter – you must manually create aligned partitions on your VMDK’s or use a Guest OS that creates properly aligned partitions (Windows 2008 and later).

Best Practices:

Before I wrap this installment up, here are some best practices for storage alignment in your environment:

• Create aligned partitions in your VMware templates. Do it once, do it right – every machine you deploy from the template will be aligned.
• Use caution with tools like Symantec Ghost.  Ghost can take images of aligned partitions and misalign them when laying down on a new system.
• Use caution when performing P2V’s using VMware vCenter Converter – it does not align guest disks on import.  You might consider using Converter to perform a P2V of the system disk only, then create new VMDK’s on the converted guest.  Use Diskpart, gparted, or another tool to create aligned partitions on the new VMDK’s and finally copy the data over to the newly virtualized server using a tool like Robocopy, RichCopy, or rsync.
• SSD’s are particularly sensitive to misalignment, leading to poor performance and excessive wear.
• Local VMFS volumes created by the ESX installer are not aligned.  If you are using an installer-created local VMFS for anything where performance matters, you might consider re-creating it through vCenter.
• Watch out when attaching a data disk from an older VM to a new VM.  For example, you are upgrading your SQL servers to Windows 2008 R2 from 2003.  You decide to do a side-by-side upgrade, using the detach/attach method.  You install (or better yet, deploy from template) a new Windows 2008 R2 VM, detach your databases from the old server, move your SQL data and log virtual disks from your 2003 VM to the new VM and attach the SQL DB’s on the new server.  Those old VMDK’s may be misaligned!  Consider using Robocopy, RichCopy or rsync to ensure an aligned disk.
• Check your storage vendors best practices for your particular environment (OS, workload, SAN, etc.).
• There is some debate on whether or not it is advised to align your OS partitions.  There is no clear-cut answer on this as it depends so much on your environment and particular needs.  For help in deciding if you should align your Guest OS drives, see the comments in the blogs by Duncan Epping, Vaughn Stewart, and Chad Sakac.
• While working the VMware User Group booth at the Washington, DC Virtualization Forum 2010 I had a user ask me if rules and procedures for alignment on 4k sector disks are different. I forgot to research it until just now, so I honestly don’t know (please comment if you do know!).  Check with your storage vendor if this is an issue for you.
• Finally, you can’t realign partitions using tools like mbralign or vOptimizer in ESXi -Aaaron Delp explains the problem here: http://blog.aarondelp.com/2010/06/my-1-issue-with-vmware-esxi-today.html.

I hope this is helpful for you in understanding the problem of storage alignment and how it can impact your environment.  Comments or questions are welcomed!

One of my most popular posts to date had been IBM DS3300 Write Performance Problem Solved.  I am pleased to have upgrade my internal environment to an EMC Clariion CX4 array, but still have customers using the DS3300 and the MD3000i from Dell.  For that reason, I keep an eye out for news on these arrays.  You’ll recall that these arrays are based on the same LSI kit.  Truth be told, properly designed and configured, these arrays are a technically proficient and cost efficient solution for SMB’s looking to get their VMware environments going.

While reviewing my email today I found an email from SolarWinds about their new Free SAN Monitor.  I have used several products from SolarWinds in the past, and so thought I would give this a quick try.  This tool can monitor the LSI-based arrays from Dell (including the MD3000 series), IBM (DS3000, DS4000, and DS5000), and SunStorageTek 2000 and 60000.  Installation and configuration were both fast and easy.  A wizard asked me for the array’s IP address, monitoring interval, and some thresholds (which I left blank).  Once I clicked Finish the app launched on my desktop and immediately began to display data.

This screen-shot is of the app running on my Windows 7 x64 workstation.  You’ll notice that the array is reporting performance, in terms of IOPS, and response time for individual LUNS.  The health of my array is also displayed.  You’ll see that my ‘Controller 2′ shows a failure.  I do not have a dual-controller configuration (hence my wildly popular post on performance being in the tank due to a disabled write cache).  The SAN Monitor incorrectly reported that I had two controllers, and that the 2nd is failed.

This is not bad for a free tool, in my opinion.  I would love to see SolarWinds add some info on write cache status for the array (cache hit %, cache enabled per LUN, etc.) as well as LUN utilization in terms of free space remaining.  For a free desktop GUI, it’s worth the 5 minutes to download and setup.

Reporting on I/O Response time (in milliseconds) is helpful in determining if your array is stressed.  If response times are consistently above 15ms, you ought to look at adding some spindles.  If you are over 25ms on a regluar basis, you’re likely feeling some pain on the servers generating the workload against your array.

If you are into the whole free tool thing, SolarWinds also has a free VM Monitor that is capable of monitoring a single ESX server via SNMP and it’s associated VM’s.  It too runs as a desktop application, requiring minimal setup and configuration.  While it won’t monitoring your whole VMware Essentials (3 ESX servers + vCenter) environment, it’s a start.

You might also check out Nick Weaver’s vSphere Mini Monitor for basic real-time alerts and monitors, as well as NetWrix’s Change Reporter for VMware.  A final free (and nothing short of awesome) tool is Alan Renouf’s vCheck Daily Report PowerShell Script.

Do you have free tools, scripts or utilities for your VMware environment?  I’d love to hear more about them in the comments section!

Most of what I covered in Storage Basics Parts 1 through 5 was at a very elementary level.  The math I used to do IOPS calculations, for example, is only true under very certain conditions.  RAID controllers implement caching and other techniques that skew the simple math that I provided.  I mentioned that the type of interface that you ought to use on your storage array should not be randomly chosen.  In fact, choosing the right array with the appropriate components and characteristics can only be done when you enlighten your decision with a characterization of workloads it will be running.

The character of your storage workload can be broken down into several traits – random vs. sequential I/O, large vs. small I/O request size, read vs. write ratio, and degree of parallelism.  The traits of your particular workload dictate how it interacts with the components of your storage system and ultimately determine the performance of your environment under a given configuration.  There is an excellent whitepaper available from VMware entitled “Easy and Efficient Disk I/O Workload Characterization inVMware ESX Server” that is authoritative on this subject.  If you want to get down and dirty with the topic, it’s a good read.  I’m aiming for something a bit less academic.  With that said, let’s break down workload characterization a bit so as to better understand how it will impact your real-world systems.

Random vs. Sequential Access

In Part II of this series we looked at the formula for calculating IOPS capabilities for a single disk.  That formula goes something like this:

IOPS = 1000/(Seek Latency + Rotational Latency)

You’ll recall that we divide into 1000 to remove milliseconds from the equation, leaving (Seek Latency + Rotational Latency) as the important part of the equation.  Rotational latency is based on the spindle speed of the disk – 7.2k, 10k, or 15k RPM for standard server or SAN disks.  If we consider the same Seagate Cheetah 15k drive from Part II, we see that rotational latency is 2.0ms.  The only way to change rotational latency is to buy faster (or slower) disks.  This essentially leaves seek latency as the only variable that we can “adjust”.  You’ll also recall that seek latency was the larger of the latencies (3.4ms for read seeks, and 3.9ms for write seeks) and counts more against IOPS capability than does rotational latency.  Seeking is the most expensive operation in terms of performance.

It is next to impossible to adjust seek latency on a disk because it is determined by the speed of the servos that move the heads across the platter.  We can, however, send workloads with different degrees of randomness to the platter.  The more sequential a workload is, the less time that will be spent in seek operations.  A high degree of sequentiality ultimately leads to faster disk response and higher throughput rates.  Sequential workloads may be candidates for slower disks or RAID levels.  Conversely, workloads that are highly randomized ought to be placed on fast spindles in fast RAID configurations.

You’ll notice that I said it was next to impossible to adjust seek latency on a disk.  While not common, some storage administrators employ a method know as ‘short stroking’ when configuring storage.  Short stroking uses less than the full capacity of the disk by placing data at the beginning of the disk where access is faster, and not placing data at the end of the disk where seeks times are greater.  This results in a smaller area on the disk platter for heads to travel over, effectively reducing seek time at the expense of capacity.

While not applicable to all workloads, storage arrays, or file systems, fragmentation can cause higher degrees of randomness leading to degraded performance.  This is the prime reason some vendors recommend that you regularly defragment your file system.  It should be noted that a VMware VMFS file system is resilient against the forces of fragmentation.  Whereas a Windows NTFS parition may hold hundreds, thousands or tens of thousands of files of different sizes, accessed randomly throughout the system’s cycle of operations, a VMFS datastore  typically holds no more than a couple hundred files.  Additionally, most of the files on a VMFS datastore are created contiguously if you are using thick-provisioned virtual disks (VMDK).  Thin-provisioned VMDK’s are slightly more susceptible to fragmentation, but do not typically suffer a high enough degree of fragmentation to register a performance impact.  See this VMware whitepaper for more on VMFS fragmentation: Performance Study of VMware vStorage Thin Provisioning.

Examples of sequential workloads include backup-to-disk operations and the writing of SQL transaction log files.  Random workloads may include collective reads from Exchange Information Stores or OLTP database access.  Workloads are often a mix of random and sequential access, as is the case with most VMware vSphere implmentations.  The degree to which they are random or sequential dictates the type of tuning you should perform to obtain the best possible performance for your environment.

I/O Request Size

I/O request size is another important factor in workload characterization.  Generally speaking, larger reads/writes are more efficient than smaller I/O to a certain point.  The use of larger I/O requests (64KB instead of 2KB, for example) can result in faster throughput and reduced processor time.  Most workloads do not allow you to adjust your I/O request size.  However, knowing your I/O request size can help with appropriate configuration of certain parameters such as array stripe size and file system cluster size.  Check with your storage vendor for more information as it pertains to your specific configuration.

If you are in a Windows shop, you can use perfmon counters such as Avg. Disk Bytes/Read to determine average I/O size.  If you are running a VMware-virtualized workload, you can take advantage of a great tool – vscsiStats – to identify your I/O request size.  More on vscsiStats later in this article.

Read vs. Write

Every workload will display a differing amount of read and write activity.  Sometimes a specific workload, say Microsoft Exchange, can be broken down into sub-workloads for logging (write-heavy) and reading the database (read-heavy).  Understanding the read-to-write ratio may help with designing the underlying storage system.  For example, a write-heavy workload may perform better on a RAID10 LUN than a RAID5 array due to the write penalty associated with RAID5.  The ratio of read:write may also dictate caching strategies.  The read:write ratio, when combined with a degree of randomness measure, can be quite useful in architecting your storage strategy for a given application or workload.

Parallelism/Outstanding I/O’s

Some workloads are capable of performing multi-threaded I/O.  These types of workloads can place a higher amount of stress on the storage system and should be understood when designing storage, both in terms of IOPS and throughput.  Multipathing may help with multi-threaded I/O workloads.  A typical VMware vSphere environment is a good example of a workload capable of queuing up outstanding I/O.

Measuring the Characteristics of Your Workload

So how do we actually characterize storage workloads?  Start with the application vendor – many have published studies that can shed light on specific storage workloads in a standard implementation.  If you are interested in measuring your own for planning/architecture reasons, or performance troubleshooting reasons, read on….  There are several tools to measure storage characteristics, depending on your operating system and storage environment.  Standard OS performance counters, such as Windows Performance Monitor (perfmon) can reveal some of the characteristics.  Array based tools such as NaviAnalyzer on EMC gear can also reveal statistics on the storage end of the equation.

One of the most exciting tools for storage workload characterization comes from VMware in the form of vscsiStats.  vscsiStats is a tool that has been included in VMware ESX server since version 3.5.  Because all I/O commands pass through the Virtual Machine Monitor (VMM), the hypervisor can inspect and report on the I/O characteristics of a particular workload, down to a unique VM running on an ESX host.  There is a ton of great information on using vscsiStats, so I won’t re-hash it all here.  I recommend starting with Using vscsiStats for Storage Performance Analysis as it contains an overview and usage instructions.  If you want to dig a bit deeper into vscsiStats, read both Storage Workload Characterization and Consolidation in Virtualized Environments and vscsiStats: Fast and Easy Disk Workload Characterization on VMware ESX Server.

vscsiStats can generate an enormous amount of data which is best viewed as a histogram.  If you’re a glutton for punishment, the data can be reviewed manually on the COS.  To extract vscsiStat output data, use the -c option to export to a .csv file.  From there you can analyze the data and create histograms using Excel.  Paul Dunn has a nifty Excel macro for analyzing and reporting on vscsiStats output here.  Gabrie van Zanten more detailed instructions for using Paul’s macro here.  Here are a couple histogram examples that I just generated from a test VM.

vscsiStats is only included with ESX, not ESXi.  However, Scott Drummond was kind enough to post a download of vscsiStats for ESXi on his Virtual Pivot blog: http://vpivot.com/2009/10/21/vscsistats-for-esxi/.  Using vscsiStats on ESXi requires dropping into Tech Support Mode (unsupported) and enabling ESXi for scp to transfer the binary to the ESXi server.

VMware esxtop can display some information but is limited in scope and does not currently support NFS.  A community-supported python script called nfstop can parse vscsiStats data and display esxtop-like data per VM on screen.

Experiment

If you are interested in generating workloads with various characteristics, check out Iometer and Bonnie++.  These tools will allow you to generate I/O that you can monitor with the tools I covered in this article.

Put it to Use

If you are provisioning a new workload or expanding an existing, invest some time in understanding your storage workload characteristics and convey those characteristics to your storage team.  A request for storage that includes the workload characteristics I discussed here, as well as expected IOPS requirements, will go much further in ensuring performance for your applications – physical or virtual – than simply asking for a certain capacity of disk.

My Storage Basics series has been neglected for some time (sick kids, snow storms, VMware Upgrades, SAN implementations and some Cisco switch upgrades took all my free time), so let’s jump right in to Part V – Cache, Controllers, and Coalescing.   Between the alliteration and fancy words, it might seem like I am about to tell a tale of international espionage.  Unfortunately, my introductory treatment of these aspects of a storage system will probably not keep you on the edge of your seat – but I’ll try to keep it interesting.

Throughout this series, we’ve been working our way from the basic building block of any storage system – the disks – outwards towards the brains of the operation – the controller.  You’ll recall that in Part II I introduced IOPS and the math that goes into calculating the IOPS capacity of a disk array.  In Part III we considered a RAID implementation’s impact on performance and availability.  And most recently in Part IV we looked at the common interface types when dealing with storage arrays.  If we put the previous parts together we still don’t have a functional storage system.  The missing piece is the controller.  Simply put, the storage controller is the hardware adapter between the disks and the servers that connect to the storage.  The controller has a specific ‘interface‘ type, is responsible for RAID operations, and handles advanced storage functionality.  A controller can be as simple as the Dell PERC or HP Smart Array add-in card on your server, or as complex as the Storage Processor in an enterprise class Storage Area Network (SAN) such as an EMC CLARiiON or NetApp FAS.

Controllers

As we look at controllers and the advanced features they provide we’ll see that some of the earlier performance equations start to break down.  The simplest controllers take disk read/write commands from the operating system and send commands down to the disk(s) attached to be read or written. This gets data onto the disk, but often does not do so in an efficient or reliable manner.  RAID-capable controllers take on the added responsibility of configuring disks in the desired RAID level, calculating & writing parity data, and writing the data in disk-spanning stripes or mirrors depending on the RAID level.

Cache

To increase performance and improve reliability, storage vendors implement a caching system on their controllers.  Cache is memory that acts as a buffer for disk I/O, and is usually battery-backed to prevent data loss in the event of a power failure.  Because of the exponentially greater speed of RAM over spinning magnetic disks, cache can improve performance by orders of magnitude. Cache can operate on both reads and writes to disk.

When dealing with writes, the controller cache is typically used in one of two ways: write-through or write-back.  In write-through mode, data is written to volatile cache and then to disk, and only acknowledged as written once the data resides on the non-volatile disk.  Write-back mode allows the controller to acknowledge the data as having been written as soon as it is held in cache.  This allows the cache to buffer writes quickly and then write them to the slower disk when the disk has cycles to accept I/O.  The greater your cache size, the more data that can be buffered, ultimately resulting in better performance as measured in both IOPS and throughput.  This graph from my article on troubleshooting write performance on an IBM DS3300 iSCSI array shows how throughput increased and latency decreased when enabling write cache.  The extent to which cache increases performance is highly dependent on the workload characteristics (I/O size, randomness, and ratio of reads:writes).

Read-cache acts as a buffer for reads in a couple ways.  First, some controllers attempt to ‘read-ahead’, anticipating future read requests from the operating system and buffering what it expects to be the next blocks of desired data.  Some entry-level controllers simply buffer the next physical chunk of data and fill cache memory with it, while more advanced controllers may attempt to predict the right block of data based on previous requests (you just asked for 3 blocks in a row, I’m guessing you’ll come asking for the 4th next so I’ll just buffer it in fast cache for you now).  Secondly, read cache holds data that has been previously read, regardless of any pre-fetching the controller may have done.  This allows for much faster subsequent access of the same data because it is held in the faster cache, eliminating the need for the controller to go to disk for the data again.  Just like with write cache, the extent to which cache increases performance is highly dependent on the workload characteristics.

A given storage array controller only has so much cache to work with.  A Dell PERC5/E, for example, has 256MB of cache that can be used for both read and write.  While this may be enough for a direct-attached storage array, SAN’s serving multiple systems demand more cache.  In contrast, an EMC CLARiiON CX4-960 has 32GB.  Some storage vendors, such as NetApp, are getting creative with cache.  NetApp’s Performance Acceleration Module (PAM) is an add-in card that provides up to a whopping 512GB of Layer 2 cache to the storage system.

Caching mechanisms can dramatically influence performance under the right conditions.  With healthy cache in place, IOPS calculations become skewed.  However, cache can be exhausted or may not hold the data you are interested in.  If cache is insufficient to satisfy read requests, or has reached its high-water mark for writes, performance can drop off.  When cache is exhausted, the backing disk must be able to satisfy the I/O workload or performance will be unacceptable.  This is where the IOPS calculations kick in, and where having the right disk type and configuration really matters.

Queuing & Coalescing

Advanced storage systems introduce additional features to reduce I/O contention and improve cache utilization.  I won’t go into all of the features here because they vary by storage vendor.  However, I will point out two common techniques – queuing and coalescing.

Queuing refers to the ability of a storage system to queue storage commands for later processing.  Queuing can take place at various points in your storage environment, from the HBA to the storage processor/controller.  A little queuing may be OK depending on your workload, but too many outstanding I/Os can negatively impact performance (this is measured in latency).  Queue depths can be adjusted on many components in your storage and VMware landscape, but check with your vendor’s support group before you make changes to these settings.

Coalescing is performed by some storage systems to modify the character of the workload.  To better understand coalescing, picture a bunch of random write activity.  Without cache in place, the disk heads will be bouncing all over the platters trying to get the data on to disk.  A little write cache will allow the storage array to acknowledge the write for the OS, but the array still needs to de-stage that data from cache to disk quickly to prevent cache exhaustion.  The back-end disks will still be doing the chicken dance, bouncing around trying to write the random workload….  Now picture an intelligent system that re-orders the random writes that are held in cache and writes them to the disk in nice sequential stripes.  The disk heads will be less prone to jumping around the platter and the behavior will start to look more like a nice waltz than the funky chicken dance.  Coalescing is used for writes, not reads, so not all workloads benefit.

Wrap-up

With this article on Controllers, Cache, and Coalescing we’ll end our look at the basic building blocks of a storage array.  Before we end the Storage Basic series I am planning a few more articles on Storage Workload Characterization (which has been mentioned, but not directly addressed in this and previous articles), Identifying a Stressed Storage System, and Best Practices for Storage Performance in a VMware Environment.

If you are interested in more reading on Controllers, Cache, and Coalescing, I recommend the following:

Additional Reading:

• Impact of cache on the performance of the HP StorageWorks XP12000 Disk Array white paper
• Performance impact of controller cache: SQL Server read only workloads
• IOps? – Dig into the article’s comments for some great dialog between some people who really know their stuff!
• Storage Performance for SQL Server
• Storage Caching 101 – Chuck Hollis (EMC)
• Improving Performance with Interrupt Coalescing for Virtual Machine Disk IO in VMware ESX Server