Materials science rescues civilization, again

Read a bunch of articles this past week from MIT Technology Review, How materials science will determine the future of human civilization, from Stanford University, New ultra thin semiconductor materials…, and Wired, This battery breakthrough could change everything.

The message varied a bit between articles but there was an underlying theme to all of them. Materials science was taking off, unlike it ever has before. Let’s take them on, one by one, last in first out.

New battery materials

I have not reported on new battery structures or materials in the past but it seems that every week or so I run across another article or two on the latest battery technology that will change everything. Yet this one just might do that.

I am no material scientist but Bill Joy has been investing in a company, Ionic Materials, for a while now (both in his job as a VC partner and as in independent invested) that has been working on a solid battery material that could be used to create rechargeable batteries.

The problems with Li(thium)-Ion batteries today are that they are a safety risk (lithium is a highly flammable liquid) and they use an awful lot of a relatively scarce mineral (lithium is mined in Chile, Argentina, Australia, China and other countries with little mined in USA). Electric cars would not be possible today with Li-On batteries.

Ionic Materials claim to have designed a solid polymer electrolyte that can combine the properties of familiar, ultra-safe alkaline batteries we use everyday and the recharge ability of  Li-Ion batteries used in phones and cars today. This would make a cheap, safe rechargeable battery that could work anywhere. The polymer just happens to also be fire retardant.

The historic problems with alkaline, essentially zinc and manganese dioxide is that they can’t be recharged too many times before they short out. But with the new polymer these batteries could essentially be recharged for as many times as Li-Ion today.

Currently, the new material doesn’t have as many recharge cycles as they want but they are working on it. Joy calls the material ional.

New semiconductor materials

Moore’s law will eventually cease. It’s only a question of time and materials.

Silicon is increasingly looking old in the tooth. As researchers shrink silicon devices down to atomic scales, they start to breakdown and stop functioning.

The advantages of silicon are that it is extremely scaleable (shrinkable) and easy to rust. Silicon rust or silicon dioxide was very important because it is used as an insulator. As an insulating layer, it could be patterned just like the silicon circuits themselves. That way everything (circuits, gates, switches and insulators) could all use the same, elemental material.

A couple of Stanford researchers, Eric Pop and Michal Mleczko, a electrical engineering professor and a post doc researcher, have discovered two new materials that may just take Moore’s law into a couple of more chip generations. They wrote about these new materials in their paper in Science Advances.

The new materials: hafnium diselenide and zirconium diselenide have many similar properties to silicon. One is that they can be easily made to scale. But devices made with the new materials still function at smaller geometries, at just three atoms thick (0.67nm) and also consume happen less power.

That’s good but they also rust better. When the new materials rust, they form a high-K insulating material. With silicon, high-K insulators required additional materials/processing and more than just simple silicon rust anymore. And the new materials also match Silicon’s band gap.

Apparently the next step with these new materials is to create electrical contacts. And I am sure as any new material, introduced to chip fabrication will take quite awhile to solver all the technical hurdles. But it’s comforting to know that Moore’s law will be around another decade or two to keep us humming away.

New multiferric materials

But just maybe the endgame in chip fabrication materials and possibly many other domains seems to be new materials coming out of ETH Zurich Switzerland.

There a researcher, Nicola Saldi,n has described a new sort of material that has both ferro-electric and ferro-magnetic properties.

Spaldin starts her paper off by discussing how civilization evolved mainly due to materials science.

Way in the past, fibers and rosin allowed humans to attach stone blades and other material to poles/arrows/axhandles to hunt  and farm better. Later, the discovery of smelting and basic metallurgy led to the casting of bronze in the bronze age and later iron, that could also be hammered, led to the iron age.  The discovery of the electron led to the vacuum tube. Pure silicon came out during World War II and led to silicon transistors and the chip fabrication technology we have today

Spaldin talks about the other major problem with silicon, it consumes lots of energy. At current trends, almost half of all worldwide energy production will be used to power silicon electronics in a couple of decades.

Spaldin’s solution to the  energy consumption problem is multiferric materials. These materials offer both ferro-electric and ferro-magnetic properties in the same materials.

Historically, materials were either ferro-electric or ferro-magnetic but never both. However, Spaldin discovered there was nothing in nature prohibiting the two from co-existing in the same material. Then she and her compatriots designed new multiferric materials that could do just that.

As I understand it, ferro-electric material allow electrons to form chemical structures which create electrical dipoles or electronic fields. Similarly, ferro-magnetic materials allow chemical structures to create magnetic dipoles or magnetic fields.

That is multiferric materials can be used to create both magnetic and electronic fields. And the surprising part was that the boundaries between multiferric magnetic fields (domains) form nano-scale, conducting channels which can be moved around using electrical fields.

Seems to me that if this were all possible and one could fabricate a substrate using multi-ferrics and write (program) any electronic circuit  you want just by creating a precise magnetic and electrical field ontop of it. And with todays disk and tape devices, precise magnetic fields are readily available for circular and linear materials. And it would seem just as easy to use multi multiferric material for persistent data storage.

Spaldin goes on to say that replacing magnetic fields in todays magnetism centric information/storage industry with electrical fields should lead to  reduced energy consumption.

Welcome to the Multiferric age.

Photo Credit(s): Battery Recycling by Heather Kennedy;

AMD Quad Core backside by Don Scansen;  and

Magnetic Field – 14 by Windell Oskay

Old world AI, Checkers, and The Champion

Read an article in The Atlantic this week (How checkers was solved) on Jonathan Schaeffer, the man who solved checkers, and his quest to beat Marion Tinsley, The Champion.

But first some personal history, while I was at university (back in the early 70’s) and first learned how to code in real (Fortran, 360/Assembler, IBM PL/I, Cobol) languages, one independent project I worked on was a checkers playing program. It made use of advanced alpha-beta search optimizations, board analysis routines and move trees.

These were the days of punched card decks and JCL, submitting programs to run as a batch job and getting results hours to days later. For one semester, I won the honor of consuming the most CPU time of any person in the school. I still have the card deck someplace but it may be hard to find a card reader, let alone a PL/I compiler/DOS system to run it.

In any case, better men than I have taken up the checkers challenge over time. And Schaeffer had made it his life’s work to conquer checkers and did it with his program, Chinook.

In my day checkers was a young kid and old person game. It was simple enough to learn but devilishly hard to master. My program got to look about 3.5 moves ahead, Schaeffer’s later program, used during an early match, was looking 16 moves ahead and was improved from there.

Besting The Champion

From the 50s through the early 90s there was one man who was the undisputed Champion of Checkers and that was Tinsley. Although he lost a few games during his time to other men, he never lost a match.

The article talks about how Schaeffer improved Chinook over time and at one time it had beaten Tinsley in two games but still lost the match. With a later version, it beat Tinsley a couple of times and then Tinsley fell ill and had to leave the game, later dying and forfeiting the match.

But even after Tinsley’s death, Schaeffer kept on improving Chinook.

Early on Schaeffer had a checkers endgame database and an opening database that were computed by Chinook as optimal move sequences from valid openings (professional checkers has a set of 3 move openings that players select at random and the game takes off from there) and endgames (positions with limited number’s of pieces to the end of the game).

These opening and endgame databases were stored for later retrieval during a game. This way if a game fell into a set opening or endgame the program could just follow the optimal play that was already computed.

Solving checkers

As computing power increased, Chinook’s end game database started earlier in the game with more pieces on the board and his opening database started working towards later into the game, following opening moves farther into the mid game.

When Schaeffer’s program solved checkers, essentially his opening database and his endgame database met in the middle of the game. And at that point he had the solution to every checkers position/game that could ever be.

AI vs. humans today

AI has changed to a different way of operating over time. When I was coding my checkers program, it was search trees/optimizations and board analysis. In fact, in 1996 IBM Deep Blue used variants of these techniques to beat Garry Kasparov, then World Chess Champion.

Today’s machine learning is less about search algorithms, game analyses, and game (or logic) databases and more about neural nets, machine learning and reinforcement learning.

New AI finally conquered Go only a couple of years ago, a game that’s very much more complex than checkers or chess. But in 2017 Google (Deepmind) AlphaGo didn’t use search trees and board analyses, it used neural nets, machine learning and reinforcement learning to beat Ke Jie, the then World #1 ranked Go Master.

Welcome to the new world of AI.

Photo Credit(s):

New chip architecture with CPU, storage & sensors in one package

Read an article the other day in MIT news, (3D chip combines computing and data storage) about a new 3D chip out of Stanford and MIT research, which includes CPU, RRAM (resistive RAM) storage class memories and sensors in one single package. Such a chip architecture vastly minimizes the off chip bottleneck to access storage and sensors.

Chip componentry

The chip’s sensors are based on carbon nanotubes. Aside from a layer of silicon at the bottom, all the rest of transistors used in the chip are also based off of carbon nanotube FET (field effect transistors).

The RRAM storage class memory is a based on a dielectric material which uses electrical resistance to store non-volatile data.

The bottom layer is a silicon based CPU. On top of the silicon is a carbon nanotube layer. Next comes the RRAM and the top layer is more carbon nanotubes making up the sensor array.

Architectural benefits

One obvious benefit is having data storage directly accessible to the CPU is that there’s no longer a need to go off chip to access data. The 2nd major advantage to the chip architecture is that the sensor array can write directly to RRAM storage, so there’s no off chip delay to provide sensor readout and storage.

Another advantage to using carbon nanotube FET’s is that they can be an order of magnitude more energy efficient than silicon transistors. Moreover, RRAM has the potential to be much denser than DRAM.

Finally, another major advantage is that this can all be built in one 3D chip because carbon nanotube and RRAM fabrication can be done at relatively cooler temperatures (~200C) vs. silicon fabrication which requires relatively high temperatures (1000C). Silicon cannot be readily fabricated in multiple layers because of the high temperatures required which will harm lower layers. But you could fabricate the lowest layer in silicon and then the rest as either carbon nanotube FETs or RRAM without harming the silicon layer.

Transistor/RRAM counts

The chip as fabricated has a million RRAM cells (bits?) and 2 million nanotube FETs. In contrast, in 2014, Intel’s 15-core Xeon Ivy Bridge EX had 4.3B transistors and current DRAM chips offer 64Gb. So there’s a ways to go before carbon nanotube and RRAM densities can get to a level available from silicon today.

However, as they have a bottom layer of silicon they can have all the CPU complexity of an Intel processor and still build RRAM and carbon nanotubes FETs on top of that. Which makes this chip architecture compatible with current CMOS fabrication techniques and a very interesting addition to current CPU architectures.

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Unclear to me why they stopped at 4 layers (1-silicon FET, 1 carbon nanotubes FET, 1 RRAM and 1 carbon nanotubes FET [sensor array]). If they can do 4 why not do 5 or more. That way they could pack in even more RRAM storage and perhaps more sensor layers.

Also, not sure what the bottom most layer of carbon nanotubes is doing. If I had to hazard a guess, it’s being used for RRAM control logic. But I could be wrong.

I could see how these chips could be used for very specialized sensor applications, with a limited need for data storage. The researchers claim many types of sensors can be created using carbon nanotubes. If that’s the case, maybe we might see these sorts of chips showing up all over the place.

Comments?

Photo Credit(s): Three dimensional integration of nanotechnologies for computing and data storage on a single chip, Nature magazine. 

Google releases new Cloud TPU & Machine Learning supercomputer in the cloud

Last year about this time Google released their 1st generation TPU chip to the world (see my TPU and HW vs. SW … post for more info).

This year they are releasing a new version of their hardware called the Cloud TPU chip and making it available in a cluster on their Google Cloud.  Cloud TPU is in Alpha testing now. As I understand it, access to the Cloud TPU will eventually be free to researchers who promise to freely publish their research and at a price for everyone else.

What’s different between TPU v1 and Cloud TPU v2

The differences between version 1 and 2 mostly seem to be tied to training Machine Learning Models.

TPU v1 didn’t have any real ability to train machine learning (ML) models. It was a relatively dumb (8 bit ALU) chip but if you had say a ML model already created to do something like understand speech, you could load that model into the TPU v1 board and have it be executed very fast. The TPU v1 chip board was also placed on a separate PCIe board (I think), connected to normal x86 CPUs  as sort of a CPU accelerator. The advantage of TPU v1 over GPUs or normal X86 CPUs was mostly in power consumption and speed of ML model execution.

Cloud TPU v2 looks to be a standalone multi-processor device, that’s connected to others via what looks like Ethernet connections. One thing that Google seems to be highlighting is the Cloud TPU’s floating point performance. A Cloud TPU device (board) is capable of 180 TeraFlops (trillion or 10^12 floating point operations per second). A 64 Cloud TPU device pod can theoretically execute 11.5 PetaFlops (10^15 FLops).

TPU v1 had no floating point capabilities whatsoever. So Cloud TPU is intended to speed up the training part of ML models which requires extensive floating point calculations. Presumably, they have also improved the ML model execution processing in Cloud TPU vs. TPU V1 as well. More information on their Cloud TPU chips is available here.

So how do you code a TPU?

Both TPU v1 and Cloud TPU are programmed by Google’s open source TensorFlow. TensorFlow is a set of software libraries to facilitate numerical computation via data flow graph programming.

Apparently with data flow programming you have many nodes and many more connections between them. When a connection is fired between nodes it transfers a multi-dimensional matrix (tensor) to the node. I guess the node takes this multidimensional array does some (floating point) calculations on this data and then determines which of its outgoing connections to fire and how to alter the tensor to send to across those connections.

Apparently, TensorFlow works with X86 servers, GPU chips, TPU v1 or Cloud TPU. Google TensorFlow 1.2.0 is now available. Google says that TensorFlow is in use in over 6000 open source projects. TensorFlow uses Python and 1.2.0 runs on Linux, Mac, & Windows. More information on TensorFlow can be found here.

So where can I get some Cloud TPUs

Google is releasing their new Cloud TPU in the TensorFlow Research Cloud (TFRC). The TFRC has 1000 Cloud TPU devices connected together which can be used by any organization to train machine learning algorithms and execute machine learning algorithms.

I signed up (here) to be an alpha tester. During the signup process the site asked me: what hardware (GPUs, CPUs) and platforms I was currently using to training my ML models; how long does my ML model take to train; how large a training (data) set do I use (ranging from 10GB to >1PB) as well as other ML model oriented questions. I guess there trying to understand what the market requirements are outside of Google’s own use.

Google’s been using more ML and other AI technologies in many of their products and this will no doubt accelerate with the introduction of the Cloud TPU. Making it available to others is an interesting play but this would be one way to amortize the cost of creating the chip. Another way would be to sell the Cloud TPU directly to businesses, government agencies, non government agencies, etc.

I have no real idea what I am going to do with alpha access to the TFRC but I was thinking maybe I could feed it all my blog posts and train a ML model to start writing blog post for me. If anyone has any other ideas, please let me know.

Comments?

Photo credit(s): From Google’s website on the new Cloud TPU

 

Disaster recovery from VMware to AWS using Dell EMC Avamar & Data Domain

avI was at Dell EMC World2017 last week and although most of the news was on Dell’s new 14th generation server and Dell-EMC integration progress, Wednesday’s keynote was devoted to storage and non-server infrastructure news.

There was plenty of non-server news but one item that caught my attention was new functionality from Dell EMC Data Protection Division that used Avamar and Data Domain to provide disaster recovery for VMware VMs directly to AWS.

Data Domain (AWS) Cloud DR

Dell EMC Data Domain Cloud DR (DDCDR) is  a new capability that enables DD to backup to AWS S3 object storage and when needed restart the virtual machines within AWS.

DDCDR requires that a customer with Avamar backup and Data Domain (DD) storage install an OVA which deploys an “add-on” to their on-prem Avamar/DD system and install a lightweight VM (Cloud DR server) utility in their AWS domain.

Once the OVA is installed, it will read the changed data and will segment, encrypt, and compress the backup data and then send this and the backup metadata to AWS S3 objects. Avamar/DD policies can be established to control how many daily backup copies are to be saved to S3 object storage. There’s no need for Data Domain or Avamar to run in AWS.

When there’s a problem at the primary data center, an admin can click on a Avamar GUI button and have the Cloud DR server, uncompress, decrypt, rehydrate and restore the backup data into EBS volumes, translate the VMware VM image to an AMI image and then restarts the AMI on an AWS virtual server (EC2) with its data on EBS volume storage. The Cloud DR server will use the backup metadata to select the AWS EC2 instance with the proper CPU and RAM needed to run the application. Once this completes, the VM is running standalone, in an AWS EC2 instance. Presumably, you have to have EC2 and EBS storage volumes resources available under your AWS domain to be able to install the application and restore its data.

For simplicity purposes, the user can control almost all of the required functionality for DDCDR from the Avamar GUI alone. But in case of a site outage, the user can initiate the application DR from a portal supplied by the Cloud DR server utility.

There you have it, simplified, easy to use (AWS) Cloud DR for your VM applications all through Dell EMC Avamar, Data Domain storage and DDCDR. At the moment, it only works with AWS cloud but it’s likely to be available for other public clouds in the near future.

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There was much more infrastructure news at Dell EMC World2017. I’ll discuss more details on their new storage offerings in my upcoming Storage Intelligence newsletter, due out the end of this month. If your interested in receiving your own copy of my newsletter, checkout the signup button in the upper right of this page.

Comments?

[Edits were made for readability and technical accuracy after this post was published. Ed]

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.

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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