QoM 16-001: Will NVMe GA in enterprise storage over the next 12 months? Yes 0.68 probability

NVMeThe latest analyst forecast contest Question of the Month (QoM 16-001) is on whether NVMe PCIe-SSDs will GA in enterprise storage over the next 12 months? For more information on our analyst forecast contest, please check out the post.

There are a couple of considerations that would impact NVMe adoption.

Availability of NVMe SSDs?

Intel, Samsung, Seagate and WD-HGST are currently shipping 2.5″ & HH-HL NVMe PCIe SSDs for servers. Hynix, Toshiba, and others had samples at last year’s Flash Memory Summit and promised production early this year. So yes, they are available, from at least 3 sources now, including enterprise class storage vendors, with more coming online over the year.

Some advantages of NVMe SSDs?

Advantages of NVMe (compiled from NVMe organization and other NVMe sources):

  • Lower SSD write and read IO access latencies
  • Higher mixed IOPS performance
  • Widespread OS support (not necessarily used in storage systems
  • Lower power consumption
  • X4 PCIe support
  • NVMe over FC Fabric (new RDMA) support

Disadvantages of NVMe SSDs?

Disadvantages of NVMe (compiled from NVMe drive reviewers and other sources):

  • Smaller form factors limit (MLC) capacity SSDs
  • New cabling (U.2) for 2.5″ SSDs
  • BIOS changes to support boot from NVMe (not much of a problem in storage systems)

Not many enterprise storage vendors use PCIe Flash

Current storage vendors that use PCIe flash (sourced from web searches on PCIe flash for major storage vendors):

  • Using PCIe SSDs as part or only storage tier
    • Kamanario K2 all flash array
    • NexGen storage hybrid storage
  • NetApp (PCIe) FlashCache
  • Others (?2) with Volatile cache backed by PCIe SSDs
  • Others (?2) using PCIe SSD as Non-volatile cache

Only a few of these will have new storage hardware out over the next 12 months. I estimated (earlier) about 1/3 of current storage vendors will release new hardware over the next 12 months.

The advantages of NVMe don’t matter as much unless you have a lot of PCIe flash in your system, so the 2 vendors above that use PCIe SSDs as storage are probably most likely to move to NVMe, but the limited size of NVMe drives, the meagre performance speed up to storage available from NVMe, may make NVMe adoption less likely.  So maybe there’s a 0.3 probability * 1/3 (of vendors with hardware refresh) * 2 (vendors using PCIe flash as storage) or ~0.2.

For the other 5 candidates listed above, the advantages for NVMe aren’t that significant, so if they are refreshing their hardware, there’s maybe a low chance that they will take on NVMe, mainly because it’s going to become the prominent PCIe flash protocol, So maybe that adds another 0.15 of probability * 1/3 * 5 or ~0.25. (When I originally formulated the NVMe QoM I had not anticipated NVMe SSDs backing up volatile cache but they certainly exist, today.)

Other potential candidate for NVMe are all start ups. EMC DSSD uses PCIe fabric for it’s NAND support, and could already be making use of NVMe. (Although, I would  not classify DSSD as an enterprise storage vendor.)

But there may be other start ups out there using PCIe flash that would consider moving to NVMe. A while back, I estimated there’s ~3 startups likely to emerge over the next year. It’s almost a certainty that they would all have some sort of flash storage., but maybe only one of them would make use of PCIe SSDs. And it’s unclear whether they would use NVMe drives as main storage or for caching. So, splitting the difference in probabilities, we will use 0.23 probability * 1 or ~0.23.

So total up my forecast we forecast for NVMe adoption in GA enterprise storage hardware over the next 12 months to be Yes with 0.68 probability. 

The other likely candidates that will support NVMe are software defined storage or hyper converged storage. I don’t list these as enterprise storage vendors but I could be convinced that this was a mistake. If I add in SW defined storage the probability goes up, to high 0.80s to low 0.90s.

Comments?

 

(Storage QoM 16-001): Will we see NVM Express (NVMe) drives GA’d in enterprise storage over the next year

NVMeFirst, let me state that QoM stands for Question of the Month. Doing these forecast can be a lot of work, and rather than focusing my whole blog on weekly forecast questions and answers, I would like to do something else as well. So, from now on we are doing only one new forecast a month.

So for the first question of 2016, we will forecast whether NVMe SSDs will be GA’d in enterprise storage over the next year.

NVM Express (NVMe) means the new PCIe interface for SSD storage. Wikipedia has a nice description of NVMe. As discussed there, NVMe was designed for higher performance and enhanced parallelism which comes with the PCI Express (PCIe) bus. The current version of the NVMe spec is 1.2a (available here).

GA means generally available for purchase by any customer.

Enterprise storage systems refers to mid-range and enterprise class storage systems from major AND non-major storage vendors, which includes startups.

Over the next year means by 19 January 2017.

Special thanks to Kacey Lai (@mrdedupe), Primary Data for suggesting this months question.

Current and updates to previous forecasts

 

Update on QoW 15-001 (3DX) forecast:

News out today indicates that 3DX (3D XPoint non-volatile memory) samples may be available soon but it could take another 12 to 18 months to get it into production. 3DX manufacturing is more challenging than current planar NAND technology and uses about 100 new materials, many of which are currently single sourced. We already built into our 3DX forecast potential delays in reaching production in 6 months. The news above says this could be worse than  expected. As such, I feel even stronger that there is less of a possibility of 3DX shipping in storage systems by next December. So I would update my forecast for QoW 15-001 to NO with an 0.75 probability at this time.

So current forecasts for QoW 15-001 are:

A) YES with 0.85 probability; and

B) NO with 0.75 probability

Current QoW 15-002 (3D TLC) forecast

We have 3 active participants, current forecasts are:

A) Yes with 0.95 probability;

B) No with 0.53 probability; and

C) Yes with 1.0 probability

Current QoW 15-003 (SMR disk) forecast

We have 1 active participant, current forecast is:

A) Yes with 0.85 probability

 

SCI’s (Storage QoW 15-001) 3D XPoint in next years storage, forecast=NO with 0.62 probability

20147811875_413b041e3f_z
So as to my forecast for the first question of the week: (#Storage-QoW 2015-001) – Will 3D XPoint be GA’d in  enterprise storage systems within 12 months?

I believe the answer will be Yes with a 0.38 probability or conversely, No with a 0.62 probability.

We need to decompose the question to come up with a reasonable answer.

1. How much of an advantage will 3D XPoint provide storage systems?

The claim is 1000X faster than NAND, 1000X endurance of NAND, & 10X density of DRAM. But, I believe the relative advantage of the new technology depends mostly on its price. So now the question is what would 3D XPoint technology cost ($/GB).

It’s probably going to be way more expensive than NAND $/GB (@2.44/64Gb-MLC or ~$0.31/GB). But how will it be priced relative to  DRAM (@$2.23/4Gb DDR4 or ~$4.46/GB) and (asynch) SRAM (@$7.80/ 16Mb or $3900.00/GB)?

More than likely, it’s going to cost more than DRAM because it’s non-volatile and almost as fast to access. As for how it relates to SRAM, the pricing gulf between DRAM and asynch SRAM is so huge, I think pricing it even at 1/10th SRAM costs, would seriously reduce the market. And I don’t think its going to be too close to DRAM, so maybe ~10X the cost of DRAM, or $44.60/GB.  [Probably more like a range of prices with $44.60 at 0.5 probable, $22.30 at 0.25 and $66.90 at 0.1. Unclear how I incorporate such pricing variability into a forecast.]

At $44.60/GB, what could 3D XPoint NVM replace in a storage system: 1) non-volatile cache; 2) DRAM caches, 3) Flash caches; 4) PCIe flash storage or 5) SSD storage in storage control units.

Non-volatile caching uses battery backed DRAM (with or without SSD offload) and SuperCap backed DRAM with SSD offload. Non-volatile caches can be anywhere from 1/16 to 1/2 total system cache size. The average enterprise class storage has ~412GB of cache, so non-volatile caching could be anywhere from 26 to 206GB or lets say ~150GB of 3D XPoint, which at ~$45/GB, would cost $6.8K in chips alone, add in $1K of circuitry and it’s $7.8K

  • For battery backed DRAM – 150GB of DRAM would cost ~$670 in chips, plus an SSD (~300GB) at ~$90, and 2 batteries (8hr lithium battery costs $32) so $64. Add charging/discharging circuitry, battery FRU enclosures, (probably missing something else) but maybe all the extras come to another $500 or ~$1.3K total. So the at $45/GB the 3D Xpoint non-volatile cache would run ~6.0X the cost of battery backed up DRAM.
  • For superCAP backed DRAM – similarly, a SuperCAP cache would have the same DRAM and SSD costs ($670 & $90 respectively). The costs for SuperCAPS in equivalent (Wh) configurations, run 20X the price of batteries, so $1.3K. Charging/discharging circuitry and FRU enclosures would be simpler than batteries, maybe 1/2 as much, so add $250 for all the extras, which means a total SuperCAP backed DRAM cost of ~$2.3K., which puts 3D Xpoint at 3.4X the cost of superCAP backed DRAM.

In these configurations a 3D XPoint non-volatile memory would replace lot’s of circuitry (battery-charging/discharging & other circuitry or SuperCAP-charging/discharging & other circuitry) and the SSD. So, 3D XPoint non-volatile cache could drastically simplify hardware logic and also software coding for power outages/failures. Less parts and coding has some intrinsic value beyond pure cost, difficult to quantify, but substantive, nonetheless.

As for using 3D XPoint to replace volatile DRAM cache another advantage is you wouldn’t need to have a non-volatile cache and systems wouldn’t have to copy data between caches. But at $45/GB, costs would be significant. A 412GB DRAM cache would cost $1.8K in DRAM chips and maybe another $1K in circuitry, so~ $2.8K. Doing one in 3D XPoint would run $18K in chips and the same $1K in circuitry, so $19K.  But we eliminate the non-volatile cache. Factoring that in, the all 3D XPoint cache would run ~$19K vs. DRAM volatile and (SuperCAP backed) non-volatile cache $2.8K+$2.3K= $5.1 or ~3.7X higher costs.

Again, the parts cost differential is not the whole story. But replacing volatile cache AND non-volatile cache would probably require more coding not less.

As for using 3D XPoint as a replacement or FlashCache I don’t think it’s likely because the cost differential at $45/GB is ~100X Flash costs (not counting PCIe controller and other logic) . Ditto for PCIe Flash and SSD storage.

Being 1000X denser than DRAM is great, but board footprint is not a significant storage system cost factor today.

So at a $45/GB price maybe there’s a 0.35 likelihood that storage systems would adopt the technology.

2. How many vendors are likely to GA new enterprise storage hardware in the next 12 months?

We can use major vendors to help estimate this. I used IBM, EMC, HDS, HP and NetApp as representing the major vendors for this analysis.

IBM (2 for 4) 

  • They just released a new DS8880 last fall and their prior version DS8870 came out in Oct. 2013, so the DS8K seems to be on a 24 month development cycle. So, its very unlikely we will see a new DS8K be released in next 12 month. 
  • SVC engine hardware DH8 was introduced in May 2014. SVC CG8 engine was introduced in May 2011. So SVC hardware seems to be on a 36 month cycle. So, its very unlikely we will see a new SVC hardware engine will be released in the next 12 months.
  • FlashSystem 900 hardware was just rolled out 1Q 2015  and FlashSystem 840 was introduced in January of 2014. So FlashSystem hardware is on a ~15 month hardware cycle. So, it is very likely that a new FlashSystem hardware will be released in the next 12 months. 
  • XIV Gen 3 hardware was introduced in July of 2011. Unclear when Gen2 was rolled out but IBM acquired XIV in Jan of 2008 and released an IBM version in August, 2008. So XIV’s on a ~36 month cycle. So, it is very likely that a new generation of XIV will be released in the next 12 months. 

EMC ([4] 3 for 4) 

  • VMAX3 was GA’d in 3Q (Sep) 2014. VMAX2 was available Sep 2012, which puts VMAX on 24 month cycle. So, it’s very likely that a new VMAX will be released in the next 12 months.
  • VNX2 was announced May, 2013 and GA’d Sep 2013. VNX 1 was announced Jan ,2011 and GA’d by May 2011. So that puts VNX on a ~28 month cycle. Which means we have should have already seen a new one, so it’s very likely we will see a new version of VNX in the next 12 months.  
  • XtremIO hardware was introduced in Mar, 2013 with no new significant hardware changes since. With a lack of history to guide us let’s assume a 24 month cycle. So, it’s very likely we will see a new version of XtremIO hardware in the next 12 months.
  • Isilon S200/X200 was introduced April, 2011 and X400 was released in May, 2012. Which put Isilon on a 13 month cycle then but nothing since.  So, it’s very likely we will see a new version of Isilon hardware in the next 12 months. 

However, having EMC’s unlikely to update all their storage hardware in the same 12 moths. That being said, XtremIO could use a HW boost as IBM and the startups are pushing AFA technology pretty hard here. Isilon is getting long in the tooth, so that’s another likely changeover. Since VNX is more overdue than VMAX, I’d have to say it’s likely new VNX, XtremIO & Isilon hardware will be seen over the next year. 

HDS (1 of 3) 

  • Hitachi VSP G1000 came out in Apr of 2014. HDS VSP came out in Sep of 2010. So HDS VSP is on a 43 month cycle. So it’s very unlikely we will see a new VSP in 12 months. 
  • Hitachi HUS VM came out in Sep 2012.  As far as I can tell there were no prior generation systems. But HDS just came out with the G200-G800 series, leaving the HUS VM as the last one not updated so, it’s very likely we will see a new version of HUS VM in the next 12 months.
  • Hitachi VSP G800, G600, G400, G200 series came out in Nov of 2015. Hitachi AMS 2500 series came out in April, 2012. So the mid-range systems seem to be on an 43 month cycle. So it’s very unlikely we will see a new version of HDS G200-G800 series in the next 12 months.

HP (1 of 2) 

  • HP 3PAR 20000 was introduced August, 2015 and the previous generation system, 3PAR 10000 was introduced in June, 2012. This puts the 3PAR on a 38 month cycle. So it’s very unlikely we will see a new version of 3PAR in the next 12 months. 
  • MSA 1040 was introduced in Mar 2014. MSA 2040 was introduced in May 2013. This puts the MSA on ~10 month cycle. So it’s very likely we will see a new version of MSA in the next 12 months. 

NetApp (2 of 2)

  • FAS8080 EX was introduced June, 2014. FAS6200 was introduced in Feb, 2013. Which puts the highend FAS systems on a 16 month cycle. So it’s very likely we will see a new version high-end FAS in the next 12 months.
  • NetApp FAS8040-8060 series scale out systems were introduced in Feb 2014. FAS3200 series was introduced in Nov of 2012. Which puts the FAS systems on a 15 month cycle. A new midrange release seems overdue, so it’s very likely we will see a new version of mid-range FAS in the next 12 months.

Overall the likelihood of new hardware being released by major vendors is 2+3+1+1+2=9/15 or ~0.60 probability of new hardware in the next 12 months.

Applying 0.60 to non-major storage vendors that typically only have one storage system GA’d at a time, which includes Coho Data, DataCore, Data Gravity, Dell, DDN, Fujitsu, Infinidat, NEC, Nexenta, NexGen Storage, Nimble, Pure, Qumulo, Quantum, SolidFire, Tegile, Tintri, Violin Memory, X-IO, and am probably missing a couple more. So of these ~21 non-major/startup vendors, we are likely to see ~13 new (non-major) hardware systems in the next 12 months. 

Some of these non-major systems are based on standard off-the-shelf, Intel server hardware and some vendors (Infinidat, Violin Memory & X-IO) have their own hardware designed systems. Of the 9 major vendor products identified above, six (IBM XIV, EMC VNX, EMC Isilon, EMC XtremIO, HP MSA and NetApp mid-range) use off the shelf, server hardware.

So all told my best guess is we should see (9+13=)22 new enterprise storage systems introduced in next 12 months from major and non-major storage vendors. 

3. How likely is it that Intel-Micron will come out with GA chip products in the next 6 months?

They claimed they were sampling products to vendors back at Flash Summit in August 2015. So it’s very likely (0.85 probability) that Intel-Micron will produce 3D XPoint chips in the next 12 months.

Some systems (IBM FlashSystems, NetApp high-end, and HUS VM) could make use of raw chips or even a new level of storage connected to a memory bus. But all of them could easily take advantage of a 3D XPoint device that was an NVMe PCIe connected storage.

But to be useable for most vendor storage systems being GA’d over the next year, any new chip technology has to be available for use in 6 months at the latest.

4. How likely is it that Intel-Micron will produce servers with 3D XPoint in the next 6 months?

Listening in at Flash Summit this seems to be their preferred technological approach to market. And as most storage vendors use standard Intel Servers this would seem to be an easiest way to adopt it. If the chips are available, I deem it 0.65 probability that Intel will GA server hardware in the next 6 months with 3D XPoint technology. 

Not sure any of the major or non-major vendors above could possible use server hardware introduced later than 6 months but Qumulo uses Agile development and releases GA code every 2 weeks, so they could take this on later than most.

But given the chip pricing, lack of significant advantage, and coding update requirements, I deem it 0.33 probability that vendors will adopt the technology even if it’s in a new server that they can use.

Summary

So there’s a 0.85 probability of chips available within 6 months for 3 potential major system that leaves us with 2.6 systems using 3D XPoint chip technology directly. 

With a 0.65 probability of servers coming out in 6 months using 3D XPoint and a 0.45 of new storage systems adopting the technology for caching. That says there’s a 0.29 probability and with 18 new systems coming out. That says 5.2 systems could potentially adopt the server technology.

For a total of 7.8 systems out of a potential 22 new systems or a 0.35 probability. 

That’s just the known GA non-major and storage startups what about the stealth(ier) startups without GA storage like Primary Data. There’s probably 2 or 3 non-GA storage startups. And if we assume the same 0.6 vendors will have GA hardware next year that is an additional 1.8 systems. More than likely these will depend on standard servers, so the 0.65 probability of Intel servers probability applies. So it’s likely we will see an additional 1.2 systems here or a total of 9.0 new systems that will adopt 3D XPoint tech in the next 12 months.

So it’s 9 systems out of 23.8 or ~0,38 probable. So my forecast is Yes at 0.38 probable. 

Pricing is a key factor here. I assumed a single price but it’s more likely a range of possibilities and factoring in a pricing range would be more accurate but I don’t know how, yet.

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I could go on for another 1000 words and still be no closer to an estimate. Somebody please check my math.

Comments?

Photo Credit(s): (iTech Androidi) 3D XPoint – Intel’s new Storage chip is 1000 faster than flash memory

Microsoft Exchange database backup performance – chart of the month

Microsoft Exchange 1001-5000 mailboxes, top 10 database backup per server
In last month’s Storage Intelligence newsletter we discussed the latest Exchange storage system performance for 1001 to 5000 mailboxes. One  charts we updated was the above Exchange database backup on a per server basis. The were two new submissions for this quarter, and both the Dell PowerEdge R730xd (#2 above) and the HP D3600 drive shelf with P441 storage controller (#10) ranked well on this metric.

This ESRP reported metric only measures backup throughput at a server level. However, because these two new submissions only had one server, it’s not as much of a problem here.

The Dell system had a SAS connected JBOD with 14-4TB 7200RPM disks and the HP system had a SAS connected JBOD with 11-6TB 7200RPM disks. The other major difference is that the HP system had 4GB of “flash backed write cache” and the Dell system only had 2GB of  “flash backed cache”.

As far as I can tell the fact that the Dell storage managed ~2.3GB/sec. and the HP storage only managed ~1.1GB/sec is probably mostly due to their respective drive configurations than anything else.

RAID 0 vs. RAID 1

One surprising characteristic of the HP setup is that they used RAID 0 while the Dell system used RAID1. This would offer a significant benefit to the Dell system during heavy read activity, but as I understand it, the database backup activity is run with a standard email stress environment. So in this case, there is a healthy mix of reads/writes going on at the time the backup activity. So the Dell system would have an advantage for reads and a penalty for writes (writing two copies of all data). So Dell’s RAID advantage is probably a wash.

Whether RAID 0 vs. RAID 1 would have made any difference to other ESRP metrics (database transfers per second, read/write/log access latencies, log processing, etc.) is subject for another post.

Of course,  with Exchange DAG’s there’s built in database redundancy so maybe RAID 0 is an OK configuration for some customers. Software based redundancy does seem to be Microsoft’s direction, at least since Exchange 2010, so maybe I’m the one that’s out of touch.

Still for such a small configuration I’m not sure I would have gone with RAID 0…

Comments?

SCI SPECsfs2008 NFS throughput per node – Chart of the month

SCISFS150928-001
As SPECsfs2014 still only has (SPECsfs sourced) reference benchmarks, we have been showing some of our seldom seen SPECsfs2008 charts, in our quarterly SPECsfs performance reviews. The above chart was sent out in last months Storage Intelligence Newsletter and shows the NFS transfer operations per second per node.

In the chart, we only include NFS SPECsfs2008 benchmark results with configurations that have more than 2 nodes and have divided the maximum NFS throughput operations per second achieved by the node counts to compute NFS ops/sec/node.

HDS VSP G1000 with an 8 4100 file modules (nodes) and HDS HUS (VM) with 4 4100 file modules (nodes) came in at #1 and #2 respectively, for ops/sec/node, each attaining ~152K NFS throughput operations/sec. per node. The #3 competitor was Huawei OceanStor N8500 Cluster NAS with 24 nodes, which achieved ~128K NFS throughput operations/sec./node. At 4th and 5th place were EMC  VNX VG8/VNX5700 with 5 X-blades and Dell Compellent FS8600 with 4 appliances, each of which reached ~124K NFS throughput operations/sec. per node. It falls off significantly from there, with two groups at ~83K and ~65K NFS ops/sec./node.

Although not shown above, it’s interesting that there are many well known scale-out NAS solutions in SPECsfs2008 results with over 50 nodes that do much worse than the top 10 above, at <10K NFS throughput ops/sec/node. Fortunately, most scale-out NAS nodes cost quite a bit less than the above.

But for my money, one can be well served with a more sophisticated, enterprise class NAS system which can do >10X the NFS throughput operations per second per node than a scale-out systm. That is, if you don’t have to deploy 10PB or more of NAS storage.

More information on SPECsfs2008/SPECsfs2014 performance results as well as our NFS and CIFS/SMB ChampionsCharts™ for file storage systems can be found in our just updated NAS Buying Guide available for purchase on our web site.

Comments?

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The complete SPECsfs2008 performance report went out in SCI’s September newsletter.  A copy of the report will be posted on our dispatches page sometime this quarter (if all goes well).  However, you can get the latest storage performance analysis now and subscribe to future free monthly newsletters by just using the signup form above right.

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

 

Coho Data, the packet processing squeeze and working set exploits

Was at Coho Data this week with Storage Field Day 8 (SFD8) (see the videos here)  and we met with Andy Warfield (@andywarfield), CTO and Co-founder Coho Data. Last time we met (at SFD6) Andy talked at length about some enhancements they were working on and gave us a tutorial on HyperLogLog (HLL) data structures that can be used to identify application working sets.

Packet processing time is getting squeezed

 

IMG_5551

Andy’s always a joy to talk with and this time was no exception. Andy started out talking about the speed of networking and what it meant for network packet processing time. He showed a chart with network speeds on the horizontal axis and packet processing time (in nsec) on the vertical access. It was a log-log chart but it showed an exponential decay such that at 10GbE a system had 67.2nsec to process a packet, at 40GbE, it had 16.8nsec to process a packet and at 100GbE the system had 6.7nsec to process a single packet. He was leading up to explaining why “storage datapaths are like network datapaths in hell”.

Similar performance dynamics are impacting storage device processing. In this case, NVMe PCIe flash devices are becoming processing bound.

Andy showed a chart for 4K random reads, plotting the number of cores on the bottom against K-IOPS on the vertical axis. At about 4 cores with one P3700 Intel PCIe NVMe card, the IOP performance of the storage system (as measured for IMG_5552 (1)NIC throughput) flattened out, from that point on, even after doubling the number of cores. It turns out with just one Intel P3700 NVMe PCIe flash card and 4 core Xeon processors one can quickly max out IOs across a 40GbE network, even though there’s plenty of networking bandwidth still available. Of course, this situation becomes much worse  with the new XPoint NVM which is 1000X faster than NAND, coming out next year from Micron-Intel (subject for a future post as Intel was another SFD8 presenter).

Andy also made the point that as a component of a system increases in cost, software usually tries to improve its utilization. This dynamic is now occurring for PCIe flash cards, which generally make up about 50% of the cost of a storage controller complex.

Location, location, location, …

Net Net, (network forwarding decision) storage data transfer time is shrinking as data placement times are becoming longer. By that I think he means that determining where to place data in the storage hierarchy is becoming more complex, taking more processing cycles just when we have less time to make those decisions.

So the crux of the question is how do we make those decisions better. Coho Data has attacked this problem by implementing HLLs to better identify application working sets.

IMG_5557Last year (See prior post for more info on HLLs) Coho Data had just started working with HLL technology and hadn’t fully implemented their working set analytics. But this year, Andy displayed an On-Stream reporting service treemap chart (where rectangle size indicates relative size of a parameter) that indicated an application’s cache working set size.

Using working set history to improve IO

By using a time series of properly implemented HLLs together with snapshots of working set block information, Coho Data can tell how the working set changes over time for an application or VM. Andy showed an example of application working set size changes over the course of multiple days and each day in the evening there was a giant spike in working set size. This turned out to be backup scans.

So Coho Data could then go back and snapshot the working set information before and after the spike to see if it was different. Once it was determined to be different, they then could go further and re-apply the working set cache data prior to the spike (not sure if this is implemented just yet) after the backup scan to re-warm the workload data cache. Of course, this meant that the system would have  read all this data back into cache. But doing so would leave the application’s data location optimized for upcoming IO activity.

This was just one example of what Coho Data could do to make a better data placement decision and improve the applications IO performance. Neat stuff, if you ask me.

Can’t wait until next year to see what Coho Data is working on next.

Comments?

Flash’s only at 5% of data storage

7707062406_6508dba2a4_oWe have been hearing for years that NAND flash is at price parity with disk. But at this week’s Flash Memory Summit, Darren Thomas, VP Storage BU, Micron said at his keynote that NAND only store 5% of the bits in a data center.

Darren’s session was all about how to get flash to become more than 5% of data storage and called this “crossing the chasm”. I assume the 5% is against yearly data storage shipped.

Flash’s adoption rate

Darren, said last year flash climbed from 4% to 5% of data center storage, but he made no mention on whether flash’s adoption was accelerating. According to another of Darren’s charts, flash is expected to ship ~77B Gb of storage in 2015 and should grow to about 240B Gb by 2019.

If the ratio of flash bits shipped to data centers (vs. all flash bits shipped) holds constant then Flash should be ~15% of data storage by 2019. But this assumes data storage doesn’t grow. If we assume a 10% Y/Y CAGR for data storage, then flash would represent about ~9% of overall data storage.

Data growth at 10% could be conservative. A 2012 EE Times article said2010-2015 data growth CAGR would be 32%  and IDC’s 2012 digital universe report said that between 2012 and 2020, data will double every two years, a ~44% CAGR. But both numbers could be talking about the world’s data growth, not just data center.

How to cross this chasm?

Geoffrey Moore, author of Crossing the Chasm, came up on stage as Darren discussed what he thought it would take to go beyond early adopters (visionaries) to early majority (pragmatists) and reach wider flash adoption in data center storage. (See Wikipedia article for a summary on Crossing the Chasm.)

As one example of crossing the chasm, Darren talked about the electric light bulb. At introduction it competed against candles, oil lamps, gas lamps, etc. But it was the most expensive lighting system at the time.

But when people realized that electric lights could allow you to do stuff at night and not just go to sleep, adoption took off. At that time competitors to electric bulb did provide lighting it just wasn’t that good and in fact, most people went to bed to sleep at night because the light then available was so poor.

However, the electric bulb  higher performing lighting solution opened up the night to other activities.

What needs to change in NAND flash marketing?

From Darren’s perspective the problem with flash today is that marketing and sales of flash storage are all about speed, feeds and relative pricing against disk storage. But what’s needed is to discuss the disruptive benefits of flash/NAND storage that are impossible to achieve with disk today.

What are the disruptive benefits of NAND/flash storage,  unrealizable with disk today.

  1. Real time analytics and other RT applications;
  2. More responsive mobile and data center applications;
  3. Greener, quieter, and potentially denser data center;
  4. Storage for mobile, IoT and other ruggedized application environments.

Only the first three above apply  to data centers. And none seem as significant  as opening up the night, but maybe I am missing a few.

Also the Wikipedia article cited above states that a Crossing the Chasm approach works best for disruptive or discontinuous innovations and that more continuous innovations (doesn’t cause significant behavioral change) does better with Everett Roger’s standard diffusion of innovation approaches (see Wikepedia article for more).

So is NAND flash a disruptive or continuous innovation?  Darren seems firmly in the disruptive camp today.

Comments?

Photo Credit(s): 20-nanometer NAND flash chip, IntelFreePress’ photostream

A new storage benchmark – STAC-M3

9761778404_73283cbd17_nA week or so ago I was reading a DDN press release that said they had just published a STAC-M3 benchmark. I had never heard of STAC or their STAC-M3 so thought I would look them up.

STAC stands for Securities Technology Analyst Center and is an organization dedicated to testing system solutions for the financial services industries.

What does STAC-M3 do

It turns out that STAC-M3 simulates processing a time (ticker tape or tick) log of security transactions and identifyies the maximum and weighted bid along with various other statistics for a number (1%) of securities over various time periods (year, quarter, week, and day) in the dataset. They call it high-speed analytics on time-series data. This is a frequent use case for systems in the securities sector.

There are two versions of the STAC-M3 benchmark: Antuco and Kanaga. The Antuco version uses a statically sized dataset and the Kanaga uses more scaleable (varying number of client threads) queries over larger datasets. For example, the Antuco version uses 1 or 10 client threads for their test measurements whereas the Kanaga version scales client threads, in some cases, from 1 to 100 threads and uses more tick data in 3 different sized datasets.

Good, bad and ugly of STAC-M3

Access to STAC-M3 reports requires a login but it’s available for free. Some details are only available after you request them which can be combersome.

One nice thing about the STAC-M3 benchmark information is that it provides a decent summary of the amount of computational time involved in all the queries it performs. From a storage perspective, if one were to take this and just analyze the queries with minimal or light computation that come closer to a pure storage workload than computationally heavy queries.

Another nice thing about the STAC-M3 is that it in some cases it provides detailed statistical information about the distribution of metrics, including mean, median, min, max and standard deviation. Unfortunately, the current version of the STAC-M3 does not provide these statistics for the computational light measures that are of primary interest to me as a storage analyst. It would be very nice to see some of their statistical level reporting be adopted by SPC, SPECsfs or Microsoft ESRP for their benchmark metrics.

STAC-M3 also provides a measure of storage efficiency, or how much storage it took to store the database. This is computed as the reference size of the dataset divided by the amount of storage it took to store the dataset. Although this could be interesting most of the benchmark reports I examined all had similar numbers for storage efficiency 171% or 172%.

The STAC-M3 benchmark is a full stack test. That is it measures the time from the point a query is issued to the point the query response is completed. Storage is just one part of this activity, computing the various statistics is another part and the database used to hold the stock tick data is another aspect of their test environment. But what is being measured is the query elapsed time. SPECsfs2014 has also recently changed over to be a full stack test, so it’s not that unusual anymore.

The other problem from a storage perspective (but not a STAC perspective) is that there is minimal write activity during any of the benchmark specific testing. There’s just one query that generates a lot of storage write activity all the rest are heavy read IO only.

Finally, there’s not a lot of description of the actual storage and server configuration available in the basic report. But this might be further detailed in the Configuration Disclosure report which you have to request permission to see.

STAC-M3 storage submissions

As it’s a stack benchmark we don’t find a lot of storage array submissions. Typical submissions include a database running on some servers with SSD-DAS or occasionally a backend storage array. In the case of DDN it was KX system’s kdb 3.2 database, with Intel Enterprise Edition servers for Lustre, with 8 Intel based DDN EXAscaler servers, talking to a DDN SFA12KX-40 storage array. In contrast, a previousr submission used an eXtremeDB Financial Edition 6.0 database running on Lucera Compute™ (16 Core SSD V2, Smart OS) nodes.

Looking back over the last couple of years of  submissions (STAC-M3 goes back to 2010), forstorage arrays, aside from the latest DDN SFA12KX-40, we find an IBM FlashSystem 840, NetApp EF550, IBM FlashSystem 810, a couple of other DDN SFA12K-40 storage solutions, and a Violin V3210 & V3220 submission.  Most of the storage array submissions were all-flash arrays, but the DDN SFA12KX-40 is a hybrid (flash-disk) appliance.

Some metrics from recent STAC-M3 storage array runs

STAC-M3-YRHIBID-MAXMBPS

In the above chart, we show the Maximum MBPS achieved in the year long high bid (YRHIBID) extraction testcase. DDN won this one handily with over 10GB/sec for its extraction result.

STAC-M3-YRHIBID-MEANRTHowever in the next chart, we show the Mean Response (query elapsed) Time (in msec.) for the query that extracts the Year long High Bid data extraction test (YRHIBID). In this case the IBM FlashSystem 810 did much better than the DDN or even the more recent IBM FlashSystem 840.

Unexpectedly, the top MBPS storage didn’t achieve the best mean response time for the YRHIBID query. I would have thought the mean response time and the maximum MBPS would show the same rankings. Not clear to me why this is, it’s the mean response time not minimum or maximum. Although the maximum response time would show the same rankings. An issue with the YRHIBID is that it doesn’t report standard deviation, median or minimum response time results. Having these other metrics might have shed some more light on this anomaly but for now this is all we have.

If anyone knows of other storage (or stack) level benchmarks for other verticals please let me know and I would be glad to dig into them to see if they provide some interesting viwes on storage performance.

Comments?

 Photo Credit(s): Stock market quotes in newspaper by Andreas Poike

Max MBPS and Mean RT Charts (c) 2015 Silverton Consulting, Inc., All Rights Reserved