A college course on identifying BS

Read an article the other day from Recode (These University of Washington professors teaching a course on Calling BS) that seems very timely. The syllabus is online (Calling Bullshit — Syllabus) and it looks like a great start on identifying falsehood wherever it can be found.

In the beginning, what’s BS?

The course syllabus starts out referencing Brandolini’s Bullshit Asymmetry Principal (Law): the amount of energy needed to refute BS is an order of magnitude bigger than to produce it.

Then it goes into a rather lengthy definition of BS from Harry Frankfort’s 1986 On Bullshit article. In sum, it starts out reviewing a previous author’s discussions on Humbug and ends up at the OED. Suffice it to say Frankfurt’s description of BS runs the gamut from: Deceptive misrepresentation to short of lying.

They course syllabus goes on to reference two lengthy discussions/comments on Frankfurt’s seminal On Bullshit article, but both Cohen’s response, Deeper into BS and Eubank & Schaeffer’s A kind word for BS: …  are focused more on academic research rather than everyday life and news.

How to mathematically test for BS

The course then goes into mathematical tests for BS that range from Fermi’s questions, the Grim Test and Benford’s 1936 Law of Anomalous Numbers. These tests are all ways of looking at data and numbers and estimating whether they are bogus or not. Benford’s paper/book talks about how the first page of logarithms is always more used than others because numbers that start with 1 are more frequent than any other number.

How rumors propagate

The next section of the course (week 4) talks about the natural ecology of BS.

Here there’s reference to an article by Friggeri, et al, on Rumor Cascades, which discusses the frequency with which patently both true, false and partially true/partially false rumors are “shared” on social media (Facebook).

The professors look at a website called Snopes.com which evaluates the veracity of publishes rumors uses this to classify the veracity of rumors. Next they examine how these rumors are shared over time on Facebook.

Summarizing their research, both false and true rumors propagate sporadically on Facebook. But even verified false or mixed true/mixed false rumors (identified by Snopes.com) continue to propagate on Facebook. This seems to indicate that rumor sharers are ignoring the rumor’s truthfulness or are just unaware of the Snopes.com assessment of the rumor.

Other topics on calling BS

The course syllabus goes on to causality (correlation is not causation, a common misconception used in BS), statistical traps and trickery (used to create BS), data visualization (which can be used to hide BS), big data (GiGo leads to BS), publication bias (e.g., most published research presents positive results, where’s all the negative results research…), predatory publishing and scientific misconduct (organizations that work to create BS for others), the ethics of calling BS (the line between criticism and harassment), fake news and refuting BS.

Fake news

The section on Fake News is very interesting. They reference an article in the NYT, The Agency about how a group in Russia have been reaping havoc across the internet with fake news and bogus news sites.

But there’s more another article on NYT website, Inside a fake news sausage factory, details how multiple websites started publishing bogus news and then used advertisement revenue to tell them which bogus news generated more ad revenue – apparently there’s money to be made in advertising fake news. (Sigh, probably explains why I can’t seem to get any sponsors for my websites…).

Improving the course

How to improve their course? I’d certainly take a look at what Facebook and others are doing to identify BS/fake news and see if these are working effectively.

Another area to add might be a historical review of fake rumors, news or information. This is not a new phenomenon. It’s been going on since time began.

In addition, there’s little discussion of the consequences of BS on life, politics, war, etc. The world has been irrevocably changed in the past  on account of false information. Knowing how bad this has been this might lend some urgency to studying how to better identify BS.

There’s a lot of focus on Academia in the course and although this is no doubt needed, most people need to understand whether the news they see every day is fake or not. Focusing more on this would be worthwhile.

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I admire the University of Washington professors putting this course together. It’s really something that everyone needs to understand  nowadays.

They say the lectures will be recorded and published online – good for them. Also, the current course syllabus is for a one credit hour course but they would like to expand it to a three to four credit hour course – another great idea

Comments?

Photo credit(s): The Donation of ConstantineNew York World – Remember the Maine, Public Domain; Benjamin Franklin’s Bag of Scalps letter;  fake-news-rides-sociales by Portal GDA

Intel’s Optane (3D Xpoint) SSD specs in the wild

Read an article the other day in Ars Technica (Specs for 1st Intel 3DX SSD…) about a preview of the Intel Octane specs for their 375GB 3D Xpoint (3DX) flash card. The device is NVMe compliant, PCIe Gen3 add in card, that’s in a half height, half length, low profile form factor.

Intel’s Optane SSD vs. the competition

A couple of items from the Intel Optane spec sheet of interest to me as a storage guru:

  • 30 Drive writes per day/12.3 PBW (written) – 3DX, at launch, had advertised that it would have 1000 times the endurance of (2D-MLC?) NAND. Current flash cards (see Samsung SSD PRO NVMe 256GB Flash card specs) offer about 200TBW (for 256GB card) or 400TBW (for 512GB card). The Samsung PRO is based on 3D (V-)NAND, so its endurance is much better than  2D-MLC at these densities. That being said, the Octane drive is still ~40X the write endurance of the PRO 950. Not quite 1000 but certainly significantly better.
  • Sequential (bandwidth) performance (R/W) of 2400/2000 MB/sec – 3DX advertised 1000 times the performance of (2D-MLC,  non-NVMe?) NAND. Current 3D (V-)NAND cards (see Samsung SSD PRO above) above offers (R/W) 2200/900 MB/sec for an NVMe device. The Optane’s read bandwidth is a slight improvement but the write bandwidth is a 2.2X improvement over current competitive devices.
  • Random 4KB IOPs performance (R/W) of 550K/500K – Similar to the previous bulleted item, 3DX advertised 1000 times the performance of (2D-MLC,  non-NVMe?) NAND. Current 3D (V-)NAND cards like the Samsung SSD PRO offer Random 4KB IOPs performance  (R/W) of 270K/85K IOPS (@4 threads). Optane’s read random 4KB IOPs performance is 2X the PRO 950 but its write performance is ~5.9X better.
  • IO latency of <10 µsec. – 3DX advertised 10X better latency than the current (2D-MLC, non-NVMe) flash drives. According to storage review (Samsung 950 Pro M.2), the Samsung PRO 950 had a latency of ~22 µsec. Optane has at least 2X better latency than the current competition.
  • Density 375GB/HH-HL-LP – 3DX advertised 1000X the density of (then current DRAM). Today Micron offers a 4GiB DDR4/288 pin DIMM which is probably 1/2 the size of the HH flash drive. So maybe in the same space this could be 8GiB. This says that the Optane is about 100X denser than today’s DRAM.

Please note, when 3DX was launched, ~2 years ago, the then current NAND technology was 2D-MLC and NVMe was just a dream. So comparing launch claims against today’s current 3D-NAND, NVMe drives is not a fair comparison.

Nevertheless, the Optane SSD performs considerably better than current competitive NVMe drives and has significantly better endurance than current 3D (V-)NAND flash drives. All of which is a great step in the right direction.

What about DRAM replacement?

At launch, 3DX was also touted as a higher density, potential replacement for DRAM. But so far we haven’t seen any specs for what 3DX NVM looks like on a memory bus. It has much better density than DRAM, but we would need to see 3DX memory access times under 50ns to have a future as a DRAM replacement. Optane’s NVMe SSD at 10 µsec. is about 200X too slow, but then again it’s not a memory device configuration nor is it attached to a memory bus.

Comments?

Photo Credit(s):  Intel Optane Spec sheet from Ars Technica Article,  DDR4 DRAM from Wikimedia user:Dsimic

Mixed progress on self-driving cars

Read an article the other day on the progress in self-driving cars in NewsAtlas (DMV reports self-driving cars are learning — fast). More details are available from their source (CA [California] DMV [Dept. of Motor Vehicles] report).

The article reported on what’s called disengagement events that occurred on CA roads. This is where a driver has to take over from the self-driving automation to deal with a potential mis-queue, mistake, or accident.

Waymo (Google) way out ahead

It appears as if Waymo, Google’s self-driving car spin out, is way ahead of the pack. It reported only 124 disengages for 636K mi (~1M km) or ~1 disengage every ~5.1K mi (~8K km). This is ~4.3X better rate than last year, 1 disengage for every ~1.2K mi (1.9K km).

Competition far behind

Below I list some comparative statistics (from the DMV/CA report, noted above), sorted from best to worst:

  • BMW: 1 disengage 638 mi (1027 km)
  • Ford: 3 disengages for 590 mi (~950 km) or 1 disengage every ~197 mi (~317 km);
  • Nissan: 23 disengages for 3.3K mi (3.5K km) or 1 disengage every ~151 mi (~243 km)
  • Cruise (GM) automation: had 181 disengagements for ~9.8K mi (~15.8K km) or 1 disengage every ~54 mi (~87 km)
  • Delphi: 149 disengages for ~3.1K mi (~5.0K km) or 1 disengage every ~21 mi (~34 km);

There was no information on previous years activities so no data on how competitors had improved over the last year.

Please note: the report only applies to travel on California (CA) roads. Other competitors are operating in other countries and other states (AZ, PA, & TX to name just a few). However, these rankings may hold up fairly well when combined with other state/country data. Thousand(s) of kilometers should be adequate to assess self-driving cars disengagement rates.

Waymo moving up the (supply chain) stack

In addition, according to a Recode, (The Google car was supposed to disrupt the car industry) article, Waymo is moving from a (self-driving automation) software supplier to a hardware and software supplier to the car industry.

Apparently, Google has figured out how to reduce their sensor (hardware) costs by a factor of 10X, bringing the sensor package down from $75K to $7.5K, (most probably due to a cheaper way to produce Lidar sensors – my guess).

So now Waymo is doing about ~65 to ~1000 X more (CA road) miles than any competitor, has a much (~8 to ~243 X) better disengage rate and is  moving to become a major auto supplier in both hardware and software.

It’s going to be an interesting century.

If the 20th century was defined by the emergence of the automobile, the 21st will probably be defined by dominance of autonomous operations.

Comments?

Photo credits: Substance E′TS; and Waymo on the road

 

Crowdsourcing made better

765140960_735722ddf8_zRead an article the other day in MIT News (Better wisdom from crowds) about a new approach to drawing out better information from crowdsourced surveys. It’s based on something the researchers have named the “surprising popularity” algorithm.

Normally, when someone performs a crowdsourced survey, the results of the survey are typically some statistically based (simple or confidence weighted) average of all the responses. But this may not be correct because, if the majority are ill-informed then any average of their responses will most likely be incorrect.

Surprisingly popular?

10955401155_89f0f3f05a_zWhat surprising popularity does, is it asks respondents what they believe will be the most popular answer to a question and then asks what the respondent believes the correct answer to the question. It’s these two answers that they then use to choose the most surprisingly popular answer.

For example, lets say the answer the surveyors are looking for is the capital of Pennsylvania (PA, a state in the eastern USA) Philadelphia or not. They ask everyone what answer would be the most popular answer. In this case yes, because Philadelphia is large and well known and historically important. But they then ask for a yes or no on whether Philadelphia is the capital of PA. Of course the answer they get back from the crowd here is also yes.

But, a sizable contingent would answer that the capital of PA is  Philadelphia wrong (it is actually Harisburg). And because there’s a (knowledgeable) group that all answers the same (no) this becomes the “surprisingly popular” answer and this is the answer the surprisingly popular algorithm would choose.

What it means

The MIT researchers indicated that their approach reduced errors by 21.3% over a simple majority and 24.2% over a confidence weighted average.

What the researchers have found, is that surprisingly popular algorithm can be used to identify a knowledgeable subset of individuals in the respondents that knows the correct answer.  By knowing the most popular answer, the algorithm can discount this and then identify the surprisingly popular (next most frequent) answer and use this as the result of the survey.

Where might this be useful?

In our (USA) last election there were quite a few false news stories that were sent out via social media (Facebook and Twitter). If there were a mechanism to survey the readers of these stories that asked both whether this story was false/made up or not and asked what the most popular answer would be, perhaps the new story truthfulness could be completely established by the crowd.

In the past, there were a number of crowdsourced markets that were being used to predict stock movements, commodity production and other securities market values. Crowd sourcing using surprisingly popular methods might be used to better identify the correct answer from the crowd.

Problems with surprisingly popular methods

The one issue is that this approach could be gamed. If a group wanted some answer (lets say that a news story was true), they could easily indicate that the most popular answer would be false and then the method would fail. But it would fail in any case if the group could command a majority of responses, so it’s no worse than any other crowdsourced approach.

Comments?

Photo Credit(s): Crowd shot by Andrew WestLost in the crowd by Eric Sonstroem

 

Domesticating data

4111674475_76be20e180_zRead an article the other day from MIT News (Taming Data) about a new system that scans all your tabular data and provides an easy way to query all this data from one system. The researchers call the system the Data Civilizer.

What does it do

Tabular data seems to be the one constant in corporate data (that and for me PowerPoint and Word docs). Most data bases are tables of one form or another (some row and some column based). Lots of operational data is in spreadsheets (tables by another name) of some type.  And when I look over most IT/Networking/Storage management GUIs, tables (rows and columns) of data are the norm.

156788318_628fb0e4dc_oThe Data Civilizer takes all this tabular data and analyzes it all, column by column, and calculates descriptive characterization statistics for each column.

Numerical data could be characterized by range, standard deviation, median/average, cardinality etc. For textual data a list of words in the column by frequency might suffice. It also indexes every  word in the tables it analyzes.

Armed with its statistical characterization of each column, the Data Civilizer can then generate a similarity index between any two columns of data across the tables it has analyzed. In that way it can connect data in one table with data in another.

Once it has a similarity matrix and has indexed all the words in every table column it has analyzed, it can then map the tabular data, showing which columns look similar to other columns. Then any arbitrary query for data, can be executed on any table that contains similar data supplying the results of the query across the multiple tables it has analyzed.

Potential improvements

The researchers indicated that they currently don’t support every table data format. This may be a sizable task on its own.

In addition statistical characterization or classification seems old school nowadays. Most new AI is moving off statistical analysis to more neural net types of classification. Unclear if you could just feed all the tabular data to a deep learning neural net, but if the end game is to find similarities across disparate data sets, then neural nets are probably a better way to go. How you would combine this with brute force indexing of all tabular data words is another question.

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In the end as I look at my company’s information, even most of my Word docs are organized in some sort of table, so cross table queries could help me a lot. Let me know when it can handle Excel and Word docs and I’ll take another look.

Photo Credit(s): Linear system table representation 2 by Ronald O’ Daniel

Glenda Sims by Glendathegood

 

Toy whirligig to blood centrifuge

653dRead an article (Stanford research: Inspired by a whirligig toy, … handpowered blood centrifuge) the other day about a group of researchers taking an idea from a kid’s whirligig that spins around as you pull on it and using it for  blood centrifuge (Paperfuge) that can be used anywhere in the world.

This was all inspired when the lead researcher saw an electronic blood centrifuge being used as a door stop in a remote clinic due to lack of electricity.

So they started looking at various pre-electricity toys that rotate quickly to see if they could come up with an alternative.

The Paperfuge

The surprising thing is that they clocked a toy whirligig at over 10K RPM which no-one knew before. The team worked on the device using experimentation, computer simulation and mathematical analysis of the various aspects of the device such as string elasticityin order to improve its speed and reliability. Finally, they were able to get their device to spin at 125K RPM.

They mounted a capillary (tube) onto a paper disk where the blood is placed and then they just start pulling and pushing the device to have it centrifuge the blood into its various components.

Blood centrifuges for anywhere

A blood centrifuge separates blood components into layers based on the density of blood elements. Red blood cells are the heaviest so they end up at the bottom of the tube, blood plasma is the lightest so it ends up at the top of the tube and parasites like malaria settle in the middle. Blood centrifuges help in diagnosing disease.

Any device spinning at 125K RPM is more than adequate to centrifuge blood. As such, the PaperFuge competes with electronic blood centrifuge  that cost $1000-$5000 and of course, take electricity to run.

The Paperfuge is currently in field testing but at $0.20 each, it would be a boon to many clinics and remote medical personnel both on and off the world.

Now about that gyroscope…

Comments?

Photo Credit(s): Childrens Books and Toys;  Video from Stanford website

Dreaming of SCM but living with NVDIMMs…

Last months GreyBeards on Storage podcast was with Rob Peglar, CTO and Sr. VP of Symbolic IO. Most of the discussion was on their new storage product but what also got my interest is that they are developing their storage system using NVDIMM technologies.

In the past I would have called NVDIMMs NonVolatile RAM but with the latest incarnation it’s all packaged up in a single DIMM and has both NAND and DRAM on board. It looks a lot like 3D XPoint but without the wait.

IMG_2338The first time I saw similar technology was at SFD5 with Diablo Technologies and SANdisk, a Western Digital company (videos here and here). At that time they were calling them UltraDIMM and memory class storage. ULTRADIMMs had an onboard SSD and DRAM and they  provided a sort of virtual memory (paged) access to the substantial (SSD) storage behind the DRAM page(s). I  wrote two blog posts about UltraDIMM and MCS (called MCS, UltraDIMM and memory IO, the new path ahead part1 and part2).

 

NVDIMM defined

NVDIMMs are currently available today from Micron, Crucial, NetList, Viking, and probably others. With today’s NVDIMM there is no large SSD (like ULTRADIMMs, just backing flash) and the complete storage capacity is available from the DRAM in the NVDIMM. At power reset, the NVDIMM sort of acts like virtual memory paging in data from the flash until all the data is in DRAM.

NVDIMM hardware includes control logic, DRAM, NAND and SuperCAPs/Batteries together in one DIMM. DRAM is used for normal memory traffic but in the case of a power outage, the data from DRAM is offloaded onto the NAND in the NVDIMM using the SuperCAP/Battery to hold up the DRAM memory just long enough to transfer it to flash..

Th problem with good, old DRAM is that it is volatile, which means when power is gone so is your data. With NVDIMMs (3D XPoint and other new non-volatile storage class memories also share this characteristic), when power goes away your data is still available and persists across power outages.

For example, Micron offers an 8GB, JEDEC DDR4 compliant, 288-pin NVDIMM that has 8GB of DRAM and 16GB of SLC flash in a single DIMM. Depending on part, it has 14.9-16.2GB/s of bandwidth and 1866-2400 MT/s (million memory transfers/second). Roughly translating MT/s to IOPS, says with ~17GB/sec and at an 8KB block size, the device should be able to do ~2.1 MIO/s (million IO operations per second [never thought I would need an acronym for that]).

Another thing that makes NVDIMMs unique in the storage world is that they are byte addressable.

Hardware – check, Software?

SNIA has a NVM Programming (NVMP) Technical Working Group (TWG), which has been working to help adoption of the new technology. In addition to the NVMP TWG, there’s pmem.io, SANdisk’s NVMFS (2013 FMS paper, formerly known as DirectFS) and Intel’s pmfs (persistent memory file system) GitHub repository.  Couldn’t find any GitHub for NVMFS but both pmem.io and pmfs are well along the development path for Linux.

swarchThe TWG identified a three prong approach to NVDIMM adoption:  crawl, walk, run (see pmem.io blog post for more info).

  • The Crawl approach uses standard block and file system drivers on Linux to talk to a NVDIMM driver. This way has the benefit of being well tested, well known and widely available (except for the NVDIMM driver). The downside is that you have a full block IO or file IO stack in front of a device that can potentially do 2.1 MIO/s and it is likely to cause a lot of overhead reducing this potential significantly.
  • The Walk approach uses a persistent memory file system (pmfs?) to directly access the NVDIMM storage using memory mapped IO. The advantage here is that there’s absolutely no kernel code active during a NVDIMM data access. But building a file system or block store up around this may require some application level code.
  • The Run approach wasn’t described well in the blog post but it seems like SANdisk’s NVMFS approach which uses both standard NVMe SSDs and non-volatile memory to build a hybrid (NVDIMM-SSD) file system.

Symbolic IO as another run approach?

Symbolic IO computationally defined storage is intended to make use of NVDIMM technology and in the Store [update 12/16/16] appliance version has SSD storage as well in a hybrid NVDIMM-SSD run-like solution. The appliance has a full version of Linux SymCE which doesn’t use a file system or the PMEM library to access the data, it’s just byte addressable storage  with a PMEM file system embedded within [update 12/16/16]. This means that applications can use standard Linux file APIs to (directly) reference NVDIMM and the backend SSD storage.

It’s computationally defined because they use compute power to symbolically transform the data reducing data footprint in NVDIMM and subsequently in the SSD backing tier. Checkout the podcast to learn more

I came away from the podcast thinking that NVDIMMs are more prevalent than I thought. So, that’s what prompted this post.

Comments?

Photo Credit(s): UltraDIMM photo taken by Ray at SFD5, Architecture picture from pmem.io blog post

 

Microsoft ESRP database access latencies – chart of the month

sciesrp1030-001
The above chart was included in last month’s SCI e-Newsletter and depicts recent Microsoft Exchange (2013) Solution Reviewed Program (ESRP) results for database access latencies. Storage systems new to this 5000 mailbox and over category include, Oracle, Pure and Tegile. As all of these systems are all flash arrays we are starting to see significant reductions in database access latencies and the #4 system (Nimble Storage) was a hybrid (disk and flash) array.

As you recall, ESRP reports on three database access latencies: read database, write database and log write. All three are shown above but we sort and rank this list based on database read activity alone.

Hard to see above, but reading the ESRP reports, one finds that the top 3 systems had 1.04, 1.06 and 1.07 milliseconds. average read database latencies. So the separation between the top 3 is less than 40 microseconds.

The top 3 database write access latencies were 1.75, 1.62 and 3.07 milliseconds, respectively. So if we were ranking the above on write response times Pure would have come in #1.

The top 3 log write access latencies were 0.67, 0.41 and 0.82 milliseconds and once again if we were ranking based on log response times Pure would be #1.

It’s unclear whether Exchange customers would want to deploy AFAs for their database and log files but these three ESRP reports and Nimble’s show that there should be no problem with the performance of AFAs in these environments.

What about data reduction?

Unclear to me is how much data reduction technologies played in the AFA and hybrid solution ESRP performance. Data reduction advantages would most likely show up in database IOPS counts more so than response times but if present, may still reduce access latencies, as there would be potentially less data to be transferred to-from the backend of the storage system into-out of storage system cache.

ESRP reports do not officially report on a vendor’s data reduction effectiveness, so we are left with whatever the vendor decides to say.

In that respect, Pure FlashArray//m20 indicated in their ESRP report that their “data reduction is significantly higher” than what they see normally (4:1) because Jetstress (ESRP benchmark program) generates lots of duplicated data.

I couldn’t find anything that Tegile T3800 or Nimble Storage said similar to the above, indicating how well their data reduction technologies worked in Jetstress as compared to normal. However, they did make a reference to their compression effectiveness in database size but I have found this number to be somewhat less effective as it historically showed the amount of over provisioning used by disk-only systems and for AFA’s and hybrid – storage, it’s unclear how much is data reduction effectiveness vs. over provisioning.

For example, Pure, Tegile and Nimble also reported a “database capacity utilization” of 4.2%60% and 74.8% respectively. And Nimble did report that over their entire customer base, Exchange data has on average, a 61.2% capacity savings.

So you tell me what was the effective data reduction for their Pure’s, Tegile’s and Nimble’s respective Jetstress runs? From my perspective Pure’s report of 4.2% looks about right (that says that actual database data fit in 4.2% of SSD storage for a ~23.8:1 reduction effectiveness for Jetstress/ESRP data. I find it harder to believe what Tegile and Nimble have indicated as it doesn’t seem to be as believable as they would imply a 1.7:1 and 1.33:1 reduction effectiveness for Jetstress/ESRP data.

Oracle FS1-2 doesn’t seem to have any data reduction capabilities and reported a 100% storage capacity used by Exchange database.

So that’s it, Jetstress uses “significantly reducible” data for some AFAs systems. But in the field the advantage of data reduction techniques are much less so.

I think it’s time that ESRP stopped using significantly reducible data in their Jetstress program and tried to more closely mimic real world data.

Want more?

The October 2016 and our other ESRP reports have much more information on Microsoft Exchange performance. Moreover, there’s a lot more performance information, covering email and other (OLTP and throughput intensive) block storage workloads, in our SAN Storage Buying Guide, available for purchase on our website. More information on file and block protocol/interface performance is also included in SCI’s SAN-NAS Buying Guidealso available from our website. And if your interested in file system performance please consider purchasing our NAS Buying Guide also available on our website.

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The complete ESRP performance report went out in SCI’s October 2016 Storage Intelligence e-newsletter.  A copy of the report will be posted on our SCI dispatches (posts) page over the next quarter or so (if all goes well).  However, you can get the latest storage performance analysis now and subscribe to future free SCI Storage Intelligence e-newsletters, by just using the signup form in the sidebar or you can subscribe here.

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