Crowdsourced vision for visually impaired

Read an article the other day in Christian Science Monitor (CSM) on the Be My Eyes App. The app is from BeMyEyes.com and is available for the iPhone and Android smart phones.

Essentially there are two groups of people that use the app:

  • Visually helpful volunteers – these people signup for the app and when a visually impaired person needs help they provide visual aid by speaking to the person on the other end.
  • Visually impaired individuals – these people signup for the app and when they are having problems understanding what they are (or are not) looking at they can turn on their camera take video with their phone and it will be sent to a volunteer, they can then ask the volunteer for help in deciding what they are looking at.

So, the visually impaired ask questions about the scenes they are shooting with their phone camera and volunteers will provide an answer.

It’s easy to register as Sighted and I assume Blind. I downloaded the app, registered and tried a test call in minutes. You have to enable notifications, microphone access and camera access on your phone to use the app. The camera access is required to display the scene/video on your phone.

According to the app there are 492K sighted individuals, 34.1K blind individuals and they have been helped 214K times.

Sounds like an easy way to help the world.

There was no requests to identify a language to use, so it may only work for English speakers. And there was no way to disable/enable it for a period of time when you don’t want to be disturbed. But maybe you would just close the app.

But other than that it was simple to use and seemed effective.

Now if there were only an app that would provide the same service for the hearing impaired to supply captions or a “filtered” audio feed to ear buds.

The world need more apps like this…

Comments

AI’s Image recognition success feeds sound recognition improvements

I must do reCAPTCHA at least a dozen times a week for various websites I use. It’s become a real pain. And the fact that I know that what I am doing is helping some AI image recognition program do a better job of identifying street signs, mountains, or shop fronts doesn’t reduce my angst.

But that’s the thing with deep learning, machine learning, re-inforcement learning, etc. they all need massive amounts of annotated data that’s a correct interpretation of a scene in order to train properly.

Computers to the rescue

So, when I read a recent article in MIT News that Computers learn to recognize sounds by watching video, I was intrigued. What the researchers at MIT have done is use advanced image recognition to annotate film clips with the names of things that are making sounds on the film. They then fed this automatically annotated data into a sound identifying algorithm to improve its recognition capability.

They used this approach to train their sound recognition system to be  able to identify natural and artificial sounds like bird song, speaking in crowds, traffic sounds, etc.

They tested their newly automatically trained sound recognition against standard labeled sound sets and was able to categorize sound with a 92% accuracy for a 10 category data set and with a 74% accuracy with a 50 category dataset. Humans are able categorize these sounds with a 96% and 81% accuracy, respectively.

AI’s need for annotation

The problem with machine learning is that it needs a massive, properly annotated data set in order to learn properly. But getting annotated data takes too long or is too expensive to do for many things that we want AI for.

Using one AI tool to annotate data to train another AI tool is sort of bootstrapping AI technology. It’s acute trick but may have only limited application. I could only think of only a few more applications of similar technology:

  • Use chest strap or EKG technology to annotate audio clips of heart beat sounds at a wrist or other appendage to train a system to accurately determine pulse rates through sound alone.
  • Use wave monitoring technology to annotate pictures and audio clips of sea waves to train a system to accurately determine wave levels for better tsunami detection.
  • Use image recognition to annotate pictures of food and then use this train a system to recognize food smells (if they ever find a way to record smells).

But there may be many others. Just further refinement of what they have used could lead to finer grained people detection. For example, as (facial) image recognition gets better, it’s possible to annotate speaking film clips to train a sound recognition system to identify people from just hearing their speech. Intelligence applications for such technology are significant.

Nonetheless, I for one am happy that the next reCAPTCHA won’t be having me identify river sounds in a matrix of 9 sound clips.

But I fear there’s enough GreyBeards on Storage podcast recordings and Storage Field Day video clips already available to train a system to identify Ray’s and for sure, Howard’s voice anywhere on the planet…

Comments?

Photo Credit(s): Wave by Matthew Potter; Waves crashing on Puget Sound by mikeskatieDay 16: Podcasting by Laura Blankenship

The fragility of public cloud IT

I have been reading AntiFragile again (by Nassim Taleb). And although he would probably disagree with my use of his concepts, it appears to me that IT is becoming more fragile, not less.

For example, recent outages at major public cloud providers display increased fragility for IT. Yet these problems, although almost national in scope, seldom deter individual organizations from their migration to the cloud.

Tragedy of the cloud commons

The issues are somewhat similar to the tragedy of the commons. When more and more entities use a common pool of resources, occasionally that common pool can become degraded. But because no-one really owns the common resources no one has any incentive to improve the situation.

Now the public cloud, although certainly a common pool of resources, is also most assuredly owned by corporations. So it’s not a true tragedy of the commons problem. Public cloud corporations have a real incentive to improve their services.

However, the fragility of IT in general, the web, and other electronic/data services all increases as they become more and more reliant on public cloud, common infrastructure. And I would propose this general IT fragility is really not owned by any one person, corporation or organization, let alone the public cloud providers.

Pre-cloud was less fragile, post-cloud more so

In the old days of last century, pre-cloud, if a human screwed up a CLI command the worst they could happen was to take out a corporation’s data services. Nowadays, post-cloud, if a similar human screws up a CLI command, the worst that can happen is that major portions of the internet services of a nation go down.

Strange Clouds by michaelroper (cc) (from Flickr)

Yes, over time, public cloud services have become better at not causing outages, but they aren’t going away. And if anything, better public cloud services just encourages more corporations to use them for more data services, causing any subsequent cloud outage to be more impactful, not less

The Internet was originally designed by DARPA to be more resilient to failures, outages and nuclear attack. But by centralizing IT infrastructure onto public cloud common infrastructure, we are reversing the web’s inherent fault tolerance and causing IT to be more susceptible to failures.

What can be done?

There are certainly things that can be done to improve the situation and make IT less fragile in the short and long run:

  1. Use the cloud for non-essential or temporary data services, that don’t hurt a corporation, organization or nation when outages occur.
  2. Build in fault-tolerance, automatic switchover for public cloud data services to other regions/clouds.
  3. Physically partition public cloud infrastructure into more regions and physically separate infrastructure segments within regions, such that any one admin has limited control over an amount of public cloud infrastructure.
  4. Divide an organizations or nations data services across public cloud infrastructures, across as many regions and segments as possible.
  5. Create a National Public IT Safety Board, not unlike the one for transportation, that does a formal post-mortem of every public cloud outage, proposes fixes, and enforces fix compliance.

The National Public IT Safety Board

The National Transportation Safety Board (NTSB) has worked well for air transportation. It relies on the cooperation of multiple equipment vendors, airlines, countries and other parties. It performs formal post mortems on any air transportation failure. It also enforces any fixes in processes, procedures, training and any other activities on equipment vendors, maintenance services, pilots, airlines and other entities that can impact public air transport safety. At the moment, air transport is probably the safest form of transportation available, and much of this is due to the NTSB

We need something similar for public (cloud) IT services. Yes most public cloud companies are doing this sort of work themselves in isolation, but we have a pressing need to accelerate this process across cloud vendors to improve public IT reliability even faster.

The public cloud is here to stay and if anything will become more encompassing, running more and more of the worlds IT. And as IoT, AI and automation becomes more pervasive, data processes that support these services, which will, no doubt run in the cloud, can impact public safety. Just think of what would happen in the future if an outage occurred in a major cloud provider running the backend for self-guided car algorithms during rush hour.

If the public cloud is to remain (at this point almost inevitable) then the safety and continuous functioning of this infrastructure becomes a public concern. As such, having a National Public IT Safety Board seems like the only way to have some entity own IT’s increased fragility due to  public cloud infrastructure consolidation.

~~~~

In the meantime, as corporations, government and other entities contemplate migrating data services to the cloud, they should consider the broader impact they are having on the reliability of public IT. When public cloud outages occur, all organizations suffer from the reduced public perception of IT service reliability.

Photo Credits: Fragile by Bart Everson; Fragile Planet by Dave Ginsberg; Strange Clouds by Michael Roper

Hardware vs. software innovation – round 4

We, the industry and I, have had a long running debate on whether hardware innovation still makes sense anymore (see my Hardware vs. software innovation – rounds 1, 2, & 3 posts).

The news within the last week or so is that Dell-EMC cancelled their multi-million$, DSSD project, which was a new hardware innovation intensive, Tier 0 flash storage solution, offering 10 million of IO/sec at 100µsec response times to a rack of servers.

DSSD required specialized hardware and software in the client or host server, specialized cabling between the client and the DSSD storage device and specialized hardware and flash storage in the storage device.

What ultimately did DSSD in, was the emergence of NVMe protocols, NVMe SSDs and RoCE (RDMA over Converged Ethernet) NICs.

Last weeks post on Excelero (see my 4.5M IO/sec@227µsec … post) was just one example of what can be done with such “commodity” hardware. We just finished a GreyBeardsOnStorage podcast (GreyBeards podcast with Zivan Ori, CEO & Co-founder, E8 storage) with E8 Storage which is yet another approach to using NVMe-RoCE “commodity” hardware and providing amazing performance.

Both Excelero and E8 Storage offer over 4 million IO/sec with ~120 to ~230µsec response times to multiple racks of servers. All this with off the shelf, commodity hardware and lots of software magic.

Lessons for future hardware innovation

What can be learned from the DSSD to NVMe(SSDs & protocol)-RoCE technological transition for future hardware innovation:

  1. Closely track all commodity hardware innovations, especially ones that offer similar functionality and/or performance to what you are doing with your hardware.
  2. Intensely focus any specialized hardware innovation to a small subset of functionality that gives you the most bang, most benefits at minimum cost and avoid unnecessary changes to other hardware.
  3. Speedup hardware design-validation-prototype-production cycle as much as possible to get your solution to the market faster and try to outrun and get ahead of commodity hardware innovation for as long as possible.
  4. When (and not if) commodity hardware innovation emerges that provides  similar functionality/performance, abandon your hardware approach as quick as possible and adopt commodity hardware.

Of all the above, I believe the main problem is hardware innovation cycle times. Yes, hardware innovation costs too much (not discussed above) but I believe that these costs are a concern only if the product doesn’t succeed in the market.

When a storage (or any systems) company can startup and in 18-24 months produce a competitive product with only software development and aggressive hardware sourcing/validation/testing, having specialized hardware innovation that takes 18 months to start and another 1-2 years to get to GA ready is way too long.

What’s the solution?

I think FPGA’s have to be a part of any solution to making hardware innovation faster. With FPGA’s hardware innovation can occur in days weeks rather than months to years. Yes ASICs cost much less but cycle time is THE problem from my perspective.

I’d like to think that ASIC development cycle times of design, validation, prototype and production could also be reduced. But I don’t see how. Maybe AI can help to reduce time for design-validation. But independent FABs can only speed the prototype and production phases for new ASICs, so much.

ASIC failures also happen on a regular basis. There’s got to be a way to more quickly fix ASIC and other hardware errors. Yes some hardware fixes can be done in software but occasionally the fix requires hardware changes. A quicker hardware fix approach should help.

Finally, there must be an expectation that commodity hardware will catch up eventually, especially if the market is large enough. So an eventual changeover to commodity hardware should be baked in, from the start.

~~~~

In the end, project failures like this happen. Hardware innovation needs to learn from them and move on. I commend Dell-EMC for making the hard decision to kill the project.

There will be a next time for specialized hardware innovation and it will be better. There are just too many problems that remain in the storage (and systems) industry and a select few of these can only be solved with specialized hardware.

Comments?

Picture credit(s): Gravestones by Sherry NelsonMotherboard 1 by Gareth Palidwor; Copy of a DSSD slide photo taken from EMC presentation by Author (c) Dell-EMC

4.5M IO/sec@227µsec 4KB Read on 100GBE with 24 NVMe cards #SFD12

At Storage Field Day 12 (SFD12) this week we talked with Excelero, which is a startup out of Israel. They support a software defined block storage for Linux.

Excelero depends on NVMe SSDs in servers (hyper converged or as a storage system), 100GBE and RDMA NICs. (At the time I wrote this post, videos from the presentation were not available, but the TFD team assures me they will be up on their website soon).

I know, yet another software defined storage startup.

Well yesterday they demoed a single storage system that generated 2.5 M IO/sec random 4KB random writes or 4.5 M IO/Sec random 4KB reads. I didn’t record the random write average response time but it was less than 350µsec and the random read average response time was 227µsec. They only did these 30 second test runs a couple of times, but the IO performance was staggering.

But they used lots of hardware, right?

No. The target storage system used during their demo consisted of:

  • 1-Supermicro 2028U-TN24RT+, a 2U dual socket server with up to 24 NVMe 2.5″ drive slots;
  • 2-2 x 100Gbs Mellanox ConnectX-5 100Gbs Ethernet (R[DMA]-NICs); and
  • 24-Intel 2.5″ 400GB NVMe SSDs.

They also had a Dell Z9100-ON Switch  supporting 32 X 100Gbs QSFP28 ports and I think they were using 4 hosts but all this was not part of the storage target system.

I don’t recall the CPU processor used on the target but it was a relatively lowend, cheap ($300 or so) dual core, Intel standard CPU. I think they said the total target hardware cost $13K or so.

I priced out an equivalent system. 24 400GB 2.5″ NVMe Intel 750 SSDs would cost around $7.8K (Newegg); the 2 Mellanox ConnectX-5 cards $4K (Neutron USA); and the SuperMicro plus an Intel Cpu around $1.5K. So the total system is close to the ~$13K.

But it burned out the target CPU, didn’t it?

During the 4.5M IO/sec random read benchmark, the storage target CPU was at 0.3% busy and the highest consuming process on the target CPU was the Linux “Top” command used to display the PS status.

Excelero claims that the storage target system consumes absolutely no CPU processing to service an 4K read or write IO request. All of IO processing is done by hardware (the R(DMA)-NICs, the NVMe drives and PCIe bus) which bypasses the storage target CPU altogether.

We didn’t look at the host cpu utilization but driving 4.5M IO/sec would take a high level of CPU power even if their client software did most of this via RDMA messaging magic.

How is this possible?

Their client software running in the Linux host is roughly equivalent to an iSCSI initiator but talks a special RDMA protocol (patent pending by Excelero, RDDA protocol) that adds an IO request to the NVMe device submission queue and then rings the doorbell on the target system device and the SSD then takes it off the queue and executes it. In addition to the submission queue IO request they preprogram the PCIe MSI interrupt request message to somehow program (?) the target system R-NIC to send the read data/write status data back to the client host.

So there’s really no target CPU processing for any NVMe message handling or interrupt processing, it’s all done by the client SW and is handled between the NVMe drive and the target and client R-NICs.

The result is that the data is sent back to the requesting host automatically from the drive to the target R-NIC over the target’s PCIe bus and then from the target system to the client system via RDMA across 100GBE and the R-NICS and then from the client R-NIC to the client IO memory data buffer over the client’s PCIe bus.

Writes are a bit simpler as the 4KB write data can be encapsulated into the submission queue command for the write operation that’s sent to the NVMe device and the write IO status is relatively small amount of data that needs to be sent back to the client.

NVMe optimized for 4KB IO

Of course the NVMe protocol is set up to transfer up to 4KB of data with a (write command) submission queue element. And the PCIe MSI interrupt return message can be programmed to (I think) write a command in the R-NIC to cause the data transfer back for a read command directly into the client’s memory using RDMA with no CPU activity whatsoever in either operation. As long as your IO request is less than 4KB, this all works fine.

There is some minor CPU processing on the target to configure a LUN and set up the client to target connection. They essentially only support replicated RAID 10 protection across the NVMe SSDs.

They also showed another demo which used the same drive both across the 100Gbs Ethernet network and in local mode or direct as a local NVMe storage. The response times shown for both local and remote were within  5µsec of each other. This means that the overhead for going over the Ethernet link rather than going local cost you an additional 5µsec of response time.

Disaggregated vs. aggregated configuration

In addition to their standalone (disaggregated) storage target solution they also showed an (aggregated) Linux based, hyper converged client-target configuration with a smaller number of NVMe drives in them. This could be used in configurations where VMs operated and both client and target Excelero software was running on the same hardware.

Simply amazing

The product has no advanced data services. no high availability, snapshots, erasure coding, dedupe, compression replication, thin provisioning, etc. advanced data services are all lacking. But if I can clone a LUN at lets say 2.5M IO/sec I can get by with no snapshotting. And with hardware that’s this cheap I’m not sure I care about thin provisioning, dedupe and compression.  Remote site replication is never going to happen at these speeds. Ok HA is an important consideration but I think they can make that happen and they do support RAID 10 (data mirroring) so data mirroring is there for an NVMe device failure.

But if you want 4.5M 4K random reads or 2.5M 4K random writes on <$15K of hardware and happen to be running Linux, I think they have a solution for you. They showed some volume provisioning software but I was too overwhelmed trying to make sense of their performance to notice.

Yes it really screams for 4KB IO. But that covers a lot of IO activity these days. And if you can do Millions of them a second splitting up bigger IOs into 4K should not be a problem.

As far as I could tell they are selling Excelero software as a standalone product and offering it to OEMs. They already have a few customers using Excelero’s standalone software and will be announcing  OEMs soon.

I really want one for my Mac office environment, although what I’d do with a millions of IO/sec is another question.

Comments?

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

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.

~~~~

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