Latest Microsoft ESRP ChampionsChart™ for over 5K mailboxes – chart of the month

(c) 2013 Silverton Consulting, Inc., All Rights Reserved
(c) 2013 Silverton Consulting, Inc., All Rights Reserved

The above, from our November 2012 StorInt Performance Dispatch, is another of our ChampionsCharts™ showing optimum storage performance.  This one displays the  Q4-2012, Exchange Solution Reviewed Program champions for the over 5,000 mailbox solutions.

All of SCI’s ChampionsCharts are divided into four quadrants, the best is upper right and is labeled Champions, the next best is upper left and is labeled Marathoners, then Sprinters and finally Slowpokes.

The ESRP chart shows relative database latency across the horizontal axis and relative log playback performance on the vertical access for all ESRP submissions for the over 5,000 mailbox category.  Systems provide better relative log playback performance the higher in the chart they are placed and better relative database latencies the further to the right one goes.

We believe that ESRP performance is a great way to see how well a similarly configured storage system will perform on the more diverse and mixed workloads seen in every day data center application environments.

All SCI ChampionsCharts represent normalized storage performance for the metrics we choose and as such for ESRP indicates that given the hardware used in the submission, Champions performed relatively better than expected in both log playback and database latency.  Marathoner systems performed relatively better in log playback and not as well in database latency.  Similarly, for the Sprinters quadrant, these systems provided relatively better database latency but worse log playback performance. In the Slowpokes quadrant these systems performed relatively worse on both performance measures\.

There are a gaggle of storage systems in the Champions quadrant.  Here we identify the top 5 systems that stand out over all the rest and are readily separated into 3 groups. Specifically;

Group 1 is a single system and is the IBM DS8700, which had both best relative log playback and database latency in their group of storage systems. The IBM DS8700 was configured to support 20,000 mailboxes.

Group 2 represents four storage systems of which two are the HDS USP-V (the previous generation of HDS VSP enterprise class storage) and the other two HDS AMS 2100 storage systems (the previous generation entry-level, mid-range class storage systems from HDS). The two USP-V systems were configured for 96,000 and 32,000 mailboxes respectively. The two AMS 2100 storage systems were configured to support 17,000 and 5,800 mailboxes respectively.

Group 3 represents a single storage system and is the HP4400 EVA configured with 6000 mailboxes.

The November ESRP Performance Dispatch identified top storage system performers for both the 1,001 to 5000 and 1000 and under mailbox categories as well.  More performance information and ChampionCharts for ESRP, SPC-1 and SPC-2 are  available in our SAN Storage Buying Guide, available for purchase on our website.


The complete ESRP performance report went out in SCI’s November 2012 newsletter.  But a copy of the report will be posted on our dispatches page sometime this month (if all goes well).  However, you can get the latest storage performance analysis now and subscribe to future free newsletters by just using the signup form above right.

As always, we welcome any suggestions or comments on how to improve our ESRP  performance reports or any of our other storage performance analyses

SCI’s Latest ESRP (v3) Performance Analysis for Over 5K mailboxes – chart of the month

Bar chart showing ESRP Top 10 total database backup throughput results
(SCIESRP120125-001) (c) 2012 Silverton Consulting, All Rights Reserved

This chart comes from our analysis of Microsoft Exchange Reviewed Solutions Program (ESRP) v3 (Exchange 2010) performance results for the over 5000 mailbox category, a report sent out to SCI Storage Intelligence Newsletter subscribers last month.

The total database backup throughput reported here is calculated based on the MB read per second per database times the number of databases in a storage configuration. ESRP currently reports two metrics for database backups the first used in our calculation above is the backup throughput on a database basis and the second is backup throughput on a server basis.  I find neither of these that useful from a storage system perspective so we have calculated a new metric for our ESRP analysis which represents the total Exchange database backup per storage system.

I find three submissions (#1, #3 & #8 above) for the same storage system (HDS USP-V) somewhat unusual in any top 10 chart and as such, provides a unique opportunity to understand how to optimize storage for Exchange 2010.

For example, the first thing I noticed when looking at the details behind the above chart is that disk speed doesn’t speed up database throughput.  The #1, #3 and #8 submissions above (HDS USP-V using Dynamic or thin provisioning) had 7200rpm, 15Krpm and 7200rpm disks respectively with 512 disk each.

So what were the significant differences between the USP-V submissions (#1, #3 and #8) aside from mailbox counts and disk speed:

  • Mailboxes per server differed from 7000 to 6000 to 4700 respectively, with the top 10 median = 7500
  • Mailboxes per database differed from 583 to 1500 to 392, with the top 10 median = 604
  • Number of databases per host (Exchange server) differed from 12 to 4 to 12, with the top 10 median = 12
  • Disk size differed from 2TB to 600GB to 2TB, with the top 10 median = 2TB
  • Mailbox size differed from 1GB to 1GB to 3GB, with the top 10 median = 1.0 GB
  • % storage capacity used by Exchange databases differed from 27.4% to 80.0% to 55.1%, with the top 10 median = 60.9%

If I had to guess, the reason the HDS USP-V system with faster disks didn’t backup as well as the #1 system is that its mailbox databases spanned multiple physical disks.  For instance, in the case of the (#3) 15Krpm/600GB FC disk system it took at least 3 disks to hold a 1.5TB mailbox database.  For the #1 performing 7200rpm/2TB SATA disk system, a single disk could hold almost 4-583GB databases on a single disk.  The slowest performer (#8) also with 7200rpm/2TB SATA disks could hold at most 1-1.2TB mailbox database per disk.

One other thing that might be a factor between the #1 and #3 submissions is that the data being backed up per host was significantly different.  Specifically for a host in the #1 HDS USP-V solution they only backed up  ~4TB but for a host in the #3 submission they had to backup ~9TB.   However, this doesn’t help explain the #8 solution, which only had to backup 5.5TB per host.

How thin provisioning and average space utilization might have messed all this up is another question entirely.  RAID 10 was used for all the USP-V configurations, with a 2d+2d disk configuration per RAID group.  The LDEV configuration in the RAID groups was pretty different, i.e., for #1 & #8 there were two LDEVs one 2.99TB and the other .58TB whereas for the #3 there was one LDEV of 1.05TB.  These LDEVs were then added to Dynamic Provisioning pools for database and log files.  (There might be something in how the LDEVs were mapped to V-VOL groups but I can’t seem to figure it out.)

Probably something else I might be missing here but I believe a detailed study of these three HDS USP-V systems ESRP performance would illustrate some principles on how to accelerate Exchange storage performance.

I suppose the obvious points to come away with here are to keep your Exchange databases  smaller than your physical disk sizes and don’t overburden your Exchange servers.


The full ESRP performance report went out to our newsletter subscriber’s this past January.  A copy of the full report will be up on the dispatches page of our website sometime next month (if all goes well). However, you can get the full ESRP performance analysis now and subscribe to future newsletters by just sending us an email or using the signup form above right.

For a more extensive discussion of block or SAN storage performance covering SPC-1&-2 (top 30) and ESRP (top 20) results please consider purchasing SCI’s SAN Storage Buying Guide available on our website.

As always, we welcome any suggestions on how to improve our analysis of ESRP results or any of our other storage performance analyses.



Latest SPC-2 results – chart of the month

SPC-2* benchmark results, spider chart for LFP, LDQ and VOD throughput
SPC-2* benchmark results, spider chart for LFP, LDQ and VOD throughput

Latest SPC-2 (Storage Performance Council-2) benchmark resultschart displaying the top ten in aggregate MBPS(TM) broken down into Large File Processing (LFP), Large Database Query (LDQ) and Video On Demand (VOD) throughput results. One problem with this chart is that it really only shows 4 subsystems: HDS and their OEM partner HP; IBM DS5300 and Sun 6780 w/8GFC at RAID 5&6 appear to be the same OEMed subsystem; IBM DS5300 and Sun 6780 w/ 4GFC at RAID 5&6 also appear to be the same OEMed subsystem; and IBM SVC4.2 (with IBM 4700’s behind it).

What’s interesting about this chart is what’s going on at the top end. Both the HDS (#1&2) and IBM SVC (#3) seem to have found some secret sauce for performing better on the LDQ workload or conversely some dumbing down of the other two workloads (LFP and VOD). According to the SPC-2 specification

  • LDQ is a workload consisting of 1024KiB and 64KiB transfers whereas the LFP consists of 1024KiB and 256KiB transfers and the VOD consists of only 256KiB, so transfer size doesn’t tell the whole story.
  • LDQ seems to have a lower write proportion (1%) while attempting to look like joining two tables into one, or scanning data warehouse to create output whereas, LFP processing has a read rate of 50% (R:W of 1:1) while executing a write-only phase, read-write phase and a read-only phase, and apparently VOD has a 100% read only workload mimicking streaming video.
  • 50% of the LDQ workload uses 4 I/Os outstanding and the remainder 1 I/O outstanding. The LFP uses only 1 I/O outstanding and VOD uses only 8 I/Os outstanding.

These seem to be the major differences between the three workloads. I would have to say that some sort of caching sophistication is evident in the HDS and SVC systems that is less present in the remaining systems. And I was hoping to provide some sort of guidance as to what that sophistication looked like but

  • I was going to say they must have a better sequential detection algorithm but the VOD, LDQ and LFP workloads have 100%, 99% and 50% read ratios respectively and sequential detection should perform better with VOD and LDQ than LFP. So thats not all of it.
  • Next I was going to say it had something to do with I/O outstanding counts. But VOD has 8 I/Os outstanding and the LFP only has 1, so the if this were true VOD should perform better than LFP. While LDQ having two sets of phases with 1 and 4 I/Os outstanding should have results somewhere in between these two. So thats not all of it.
  • Next I was going to say stream (or file) size is an important differentiator but “Segment Stream Size” for all workloads is 0.5GiB. So that doesn’t help.

So now I am a complete loss as to understand why the LDQ workloads are so much better than the LFP and VOD workload throughputs for HDS and SVC.

I can only conclude that the little write activity (1%) thrown into the LDQ mix is enough to give the backend storage a breather and allow the subsystem to respond better to the other (99%) read activity. Why this would be so much better for the top performers than the remaining results is not entirely evident. But I would add that, being able to handle lots of writes or lots of reads is relatively straight forward, but handling a un-ballanced mixture is harder to do well.

To validate this conjecture would take some effort. I thought it would be easy to understand what’s happening but as with most performance conundrums the deeper you look the more confounding the results often seem to be.

The full report on the latest SPC results will be up on my website later this year but if you want to get this information earlier and receive your own copy of our newsletter – email me at

I will be taking the rest of the week off so Happy Holidays to all my readers and a special thanks to all my commenters. See you next week.

5 Reasons to Virtualize Storage

Storage virtualization has been out for at least 5 years now and one can see more and more vendors offering products in this space. I have written before about storage virtualization in “Virtualization: Tales from the Trenches” article and I would say little has changed since then but it’s time for a refresher.

Storage virtualization differs from file or server virtualization by focusing only on FC storage domain. Unlike server virtualization there is no need to change host operating environments to support most storage virtualization products.

As an aside, there may be some requirement for iSCSI storage virtualization but to date I haven’t seen much emphasis on this. Some of the products listed below may support iSCSI frontends for FC backend storage subsystems but I am unaware of any that can support FC or iSCSI frontend for iSCSI backend storage.

I can think of at least the following storage virtualization products – EMC Invista, FalconStor IPStor, HDS USP-V, IBM SVC, and NetApp ONTAP. There are more than just these but they have the lion’s share of installations, Most of these products offer similar capabilities:

  1. Ability to non-disruptively migrate data from one storage subsystem to another. This can be used to help ease technology obsolescence by online migrating data from an old subsystem to a new subsystem. There are some tools and/or services on the market which can help automate this process but storage virtualization trumps them all in that it can help tech refresh as well as provide other services.
  2. Ability to better support multiple storage tiers by migrating data from one storage tier to another. Non-disruptive data migration can also ease implementation of multiple storage tiers such as slow/high capacity disk, fast/low capacity disk and SSD storage within one storage environment. Some high end subsystems can do this with multiple storage tiers within one subsystems, but only storage virtualization can do this across storage subsystems.
  3. Ability to aggregate heterogeneous storage subsystems under one storage management environment. The other major characteristic of most storage virtualization products is that they support multiple vendor storage subsystems under one storage cluster. This can be very valuable in multi-vendor shops by providing a single management interface to provision and administer all storage under a single storage virtualization environment.
  4. Ability to scale out rather than just scale up storage performance. By aggregating storage subsystems into a single storage cluster one can add storage performance by simple adding more storage virtualization cluster nodes. Not every storage virtualization system supports multiple cluster nodes but those that do offer another dimension to storage subsystem performance.
  5. Ability to apply high-end functionality to low-end storage. This takes many forms not the least of which is sophisticated caching, point-in-time copies and data replication or mirroring capabilities typically found only in higher end storage subsystems. Such capabilities can be supplied to any and all storage underneath the storage virtualization environment and can make storage much easier to use effectively.

There are potential downsides to storage virtualization as well, not the least of which is lock-in but this may be somewhat of a red-herring. Most storage virtualization products make it easy to migrate storage into the virtualization environment. Some of these products also make it relatively easy to migrate storage out of their environment as well. This is more complex because data that was once on this storage could be almost anywhere in the current virtualized storage subsystems and would need to be re-constituted back in one piece on the storage being exported.

The other reason for lock-in is that the functionality provided by storage virtualization makes it harder to remove. But it would probably be more correct to say “once you virtualize storage you never want to go back”. Many customers I talk with that have had a good initial experience with storage virtualization want to do it again, whenever given the chance.