Crowdresearch, crowdsourced academic research

Read an article in Stanford Research, Crowdsourced research gives experience to global participants that discussed an activity in Stanford and other top tier research institutions to try to get global participation in academic research. The process is discussed more fully in a scientific paper (PDF here) by researchers from Stanford, MIT Media Lab, Cornell Tech and UC Santa Cruz.

They chose three projects:

  • A HCI (human computer interaction) project to design, engineer and build a new paid crowd sourcing marketplace (like Amazon’s Mechanical Turk).
  • A visual image recognition project to improve on current visual classification techniques/algorithms.
  • A data science project to design and build the world’s largest wisdom of the crowds experiment.

Why crowdsource academic research?

The intent of crowdsourced research is to provide top tier academic research experience to persons which have no access to top research organizations.

Participating universities obtain more technically diverse researchers, larger research teams, larger research projects, and a geographically dispersed research community.

Collaborators win valuable academic research experience, research community contacts, and potential authorship of research papers as well as potential recommendation letters (for future work or academic placement),

How does crowdresearch work?

It’s almost an open source and agile development applied to academic research. The work week starts with the principal investigator (PI) and research assistants (RAs) going over last week’s milestone deliveries to see which to pursue further next week. The crowdresearch uses a REDDIT like posting and up/down voting to determine which milestone deliverables are most important. The PI and RAs review this prioritized list to select a few to continue to investigate over the next week.

The PI holds an hour long video conference (using Google Hangouts On Air Youtube live stream service). On the conference call all collaborators can view the stream but only a select few are on camera. The PI and the researchers responsible for the important milestone research of the past week discuss their findings and the rest of the collaborators on the team can participate over Slack. The video conference is archived and available  to be watched offline.

At the end of the meeting, the PI identifies next weeks milestones and potentially directly responsible investigators (DRIs) to work on them.

The DRIs and other collaborators choose how to apportion the work for the next week and work commences. Collaboration can be fostered and monitored via Slack and if necessary, more Google live stream meetings.

If collaborators need help understanding some technology, technique, or too, the PI, RAs or DRIs can provide a mini video course on the topic or can point to other information used to get the researchers up to speed. Collaborators can ask questions and receive answers through Slack.

When it’s time to write the paper, they used Google Docs with change tracking to manage the writing process.

The team also maintained a Wiki on the overall project to help new and current members get up to speed on what’s going on. The Wiki would also list the week’s milestones, video archives, project history/information, milestone deliverables, etc.

At the end of the week, researchers and DRIs would supply a mini post to describe their work and link to their milestone deliverables so that everyone could review their results.

Who gets credit for crowdresearch?

Each week, everyone on the project is allocated 100 credits and apportions these credits to other participants the weeks activities. The credits are  used to drive a page-rank credit assignment algorithm to determine an aggregate credit score for each researcher on the project.

Check out the paper linked above for more information on the credit algorithm. They tried to defeat (credit) link rings and other obvious approaches to stealing credit.

At the end of the project, the PI, DRIs and RAs determine a credit clip level for paper authorship. Paper authors are listed in credit order and the remaining, non-author collaborators are listed in an acknowledgements section of the paper.

The PIs can also use the credit level to determine how much of a recommendation letter to provide for researchers

Tools for crowdresearch

The tools needed to collaborate on crowdresearch are cheap and readily available to anyone.

  • Google Docs, Hangouts, Gmail are all freely available, although you may need to purchase more Drive space to host the work on the project.
  • Wiki software is freely available as well from multiple sources including Wikipedia (MediaWiki).
  • Slack is readily available for a low cost, but other open source alternatives exist, if that’s a problem.
  • Github code repository is also readily available for a reasonable cost but  there may be alternatives that use Google Drive storage for the repo.
  • Web hosting is needed to host the online Wiki, media and other assets.

Initial projects were chosen in computer science, so outside of the above tools, they could depend on open source. Other projects will need to consider how much experimental apparatus, how to fund these apparatus purchases, and how a global researchers can best make use of these.

My crowdresearch projects

Some potential commercial crowdresearch projects where we could use aggregate credit score and perhaps other measures of participation to apportion revenue, if any.

  • NVMe storage system using a light weight storage server supporting NVMe over fabric access to hybrid NVMe SSD – capacity disk storage.
  • Proof of Stake (PoS) Ethereum pooling software using Linux servers to create a pool for PoS ETH mining.
  • Bipedal, dual armed, dual handed, five-fingered assisted care robot to supply assistance and care to elders and disabled people throughout the world.

Non-commercial projects, where we would use aggregate credit score to apportion attribution and any potential remuneration.

  • A fully (100%?) mechanical rover able to survive, rove around, perform  scientific analysis, receive/transmit data and possibly, effect repairs from within extreme environments such as the surface of Venus, Jupiter and Chernoble/Fukishima Daiichi reactor cores.
  • Zero propellent interplanetary tug able to rapidly transport rovers, satellites, probes, etc. to any place within the solar system and deploy theme properly.
  • A Venusian manned base habitat including the design, build process and ongoing support for the initial habitat and any expansion over time, such that the habitat can last 25 years.

Any collaborators across the world, interested in collaborating on any of these projects, do let me know, here via comments. Please supply some way to contact you and any skills you’re interested in developing or already have that can help the project(s).

I would be glad to take on PI role for the most popular project(s), if I get sufficient response (no idea what this would be). And  I’d be happy to purchase the Drive, GitHub, Slack and web hosting accounts needed to startup and continue to fruition the most popular project(s). And if there’s any, more domain experienced PIs interested in taking any of these projects do let me know.  

Comments?

Picture Credit(s): Crowd by Espen Sundve;

Videoblogger Video Conference by Markus Sandy;

Researchers Night 2014 by Department of Computer Science, NTNU;

Know Fortran, optimize NASA code, make money

Read a number of articles this past week about NASA offering a Fortran optimization contest, the High Performance Fast Computing Contest (HPFCC) for their computational fluid dynamics (CFD) program. They want to speed up CFD by 10X to 1000X and are willing to pay for it.

The contest is being run through HeroX and TopCoder and they are offering $55K, across the various levels of the contests to the winners.

The FUN3D CVD code (manual) runs on NASA’s Pliedes Linux supercomputer complex which sports over 245K cores. Even when running on the supercomputer complex, a typical CVD FUN3D run takes thousands to millions of core hours!

The program(s)

FUN3D does a hypersonic fluid analysis over a (fixed) surface which includes a “simulation of mixtures of thermally perfect gases in thermo-chemical equilibrium and non-equilibrium. The routines in PHYSICS_DEPS enable coupling of the new gas modules to the existing FUN3D infrastructure. These algorithms also address challenges in simulation of shocks and boundary layers on tetrahedral grids in hypersonic flows.”

Not sure what all that means but I am certain there’s a number of iterations on multiple Fortran modules, and it does this over a 3D grid of points, which corresponds to both the surface being modeled and the gas mixture, it’s running through at hypersonic speeds. Sounds easy enough.

The contest(s)

There are two levels to the contest: an Ideation phase (at HeroX) and an architectural phase (at TopCoder). The $55000 is split up between the HeroX ideation phase which rewards a total of $20K: $10K for winner and 2-$5K runner up prizes and the TopCoder architectural phase which rewards a total of $35K: $15K for winner and $10K for 2nd place and another $10K for “Qualified improvement candidate”.

The (HeroX) Ideation phase looks for specific new or faster algorithms that could replace current ones in FUN3D which include “exploiting algorithmic developments in such areas as grid adaptation, higher-order methods and efficient solution techniques for high performance computing hardware.”

The (TopCoder) Architecture phase looks at specifically speeding up actual FUN3D code execution. “Ideal submission(s) may include algorithm optimization of the existing code base,  Inter-node dispatch optimization or a combination of the two.  Unlike the Ideation challenge, which is highly strategic, this challenge focuses on measurable improvements of the existing FUN3d suite and is highly tactical.”

Sounds to me that the ideation phase is selecting algorithm designs and the architecture phase is running the new algorithms or just in general speeding up the FUN3D code execution.

The equation(s)

There’s a Navier-Stokes equation algorithm that get’s called maybe a trillion times until the flow settles down, during a run and any minor improvement here would be obviously significant. Perhaps there are algorithmic changes that can be used, if your an aeronautical engineer or perhaps there are compiler speedups that can be found, if your a fortran expert. Both approaches can be validated/debugged/proved out on a desktop computer.

You have to be a US citizen to access the code and you can apply here. You will receive an email to verify your email address and then once your validated and back on the website, you need to approve the software use agreement. NASA will verify your physical address by sending a letter to you with a passcode to use to finally access the code. The process may take weeks to complete, so if your interested in the contest, best to start now.

The Fortran(s)

I learned Fortran 66 a long time ago and may have dabbled with Fortran 77 but that’s the last touched fortran. But it’s like riding a bike, once you do it, it’s easy to do it again.

As I understand it the FUN3D uses Fortran 2003 and NASA suggests you use the Gnu Fortran GFortran compiler as the Intel one has some bugs in it. There appears to be a Fortran 2015 but it’s not in main use just yet.

A million core hours, just amazing. If you could save a millisecond out of the routine called a trillion times, you’d save 1 billion seconds, or ~280K core hours.

Coders start your engines…

 

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

Ethereum enters the enterprise

Read an article the other day on NYT (Business Giants Announce Creation of … Ethereum).

In case you don’t know Ethereum is a open source, block chain solution that’s different than the software behind Bitcoin and IBM’s Hyperledger (for more on Hyperledger see our Blockchains at IBM post or our GreyBeardsOnStorage podcast with Donna Dillinger, IBM Fellow).

Blockchains are a software based, permanent ledger which can be used to record anything. Bitcoin, Ethereum and Hyperledger are all based on blockchains that provide similar digital information services with varying security, programability, consensus characteristics, etc.

Earth globe within a locked cageBlockchains represent an entirely new way of doing business in the digital world and have the potential to take over many financial services  and other contracting activities that are done today between organizations.

Blockchain services provide the decentralized recording of transactions into an immutable ledger.  The decentralized nature of blockchains makes it difficult (if not impossible) to game the system to record an invalid transaction.

Miners

Ethereum supports an Ethereum Virtual Machine (EVM) application which offers customers and users a more programmable blockchain. That is rather than just updating accounts with monetary transactions like Bitcoin does, one can implement specialized transaction processing for updating the immutable ledger. It’s this programability that allows for the creation of “smart contracts” which can be programmatically verified and executed.

MinerEthereum miner nodes are responsible for validating transactions and the state transition(s) that update the ledger. Transactions are grouped in blocks by miners.

Miners are responsible for validating the transaction block and performing a hard mathematical computation or proof of work (PoW) which goes along used to validate the block of transactions. Once the PoW computation is complete, the block is packaged up and the miner node updates its database (ledger) and communicates its result to all the other nodes on the network which updates their transaction ledgers as well. This constitutes one state transition of the Ethereum ledger.

Miners that validate Ethereum transactions get paid in Ethers, which are a form of currency throughout the Ethereum ecosystem.

Blockchain consensus

Ethereum ledger consensus is based on the miner nodes executing the PoW algorithm properly. The current Ethereal PoW algorithm is Ethash, which is an “ASIC resistant” algorithm. What this means is that standard GPUs and (less so) CPUs are already very well optimized to perform this algorithm and any potential ASIC designer, if they could do better, would make more money selling their design to GPU and CPU designers, than trying to game the system.

One problem with Bitcoin is that its PoW is more ASIC friendly, which has led some organizations to developing special purpose ASICs in an attempt to dominate Bitcoin mining. If they can dominate Bitcoin mining, this can  be used to game the Bitcoin consensus system and potentially implement invalid transactions.

Ethereum Accounts

Ethereum has two types of accounts:

  • Contract accounts that are controlled by the EVM application code, or
  • Externally owned accounts (EOA) that are controlled by a set of private keys and represent external agents (miner nodes, people, transaction generating entities)

Contract accounts really are code and data which constitute the EVM bytecode (application). Contract account bytecode is also stored on the Ethereum ledger (when deployed?) and are associated with an EOA that initiates the Contract account.

Contract functionality is written in Solidity, Serpent, Lisp Like Language (LLL) or other languages that can be compiled into EVM bytecode. Smart contracts use Ethereum Contract accounts to validate and execute contract actions.

Ethereum gas pricing

As EVMs contract accounts can consume arbitrary amounts of computation, bandwidth and storage to process transactions,   Ethereum uses a concept called “gas” to pay for their resource consumption.

When a contract account transaction is initiated, it identifies a gas price (in Ethers) and a maximum gas amount that it is willing to consume to process the transaction.

When a contract transaction takes place:

  • If the maximum gas amount is less than what the transaction consumes, then the transaction is executed and is applied to the ledger. Any left over or remaining gas Ethers is credited back to the EOA.
  • If the maximum gas amount is not enough to execute the transaction, then the transaction fails and no update occurs.

Enterprise Ethereum Alliance

What’s new to Ethereum is that Accenture, Bank of New York Mellon, BP, CreditSuisse, Intel, Microsoft, JP Morgan, UBS and many others have joined together to form the Enterprise Ethereum Alliance. The alliance intends to work to create a standard version of the Ethereum software that enterprise companies can use to manage smart contracts.

Microsoft has had a Azure Blockchain-as-a-Service online since 2015.  This was based on an earlier version of Ethereum called Project Bletchley.

Ethereum seems to be an alternative to IBM Hyperledger, which offers another enterprise class block chain for smart contracts. As enterprise class blockchains look like they will transform the way companies do business in the future, having multiple enterprise class blockchain solutions seems smart to many companies.

Comments?

Photo Credit(s): Miner by Mark Callahan; Gas prices by Corpsman.com; File: Ether pharmecie.jpg by Wikimedia

 

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

 

Blockchains at IBM

img_6985-2I attended IBM Edge 2016 (videos available here, login required) this past week and there was a lot of talk about their new blockchain service available on z Systems (LinuxONE).

IBM’s blockchain software/service  is based on the open source, Open Ledger, HyperLedger project.

Blockchains explained

1003163361_ba156d12f7We have discussed blockchain before (see my post on BlockStack). Blockchains can be used to implement an immutable ledger useful for smart contracts, electronic asset tracking, secured financial transactions, etc.

BlockStack was being used to implement Private Key Infrastructure and to implement a worldwide, distributed file system.

IBM’s Blockchain-as-a-service offering has a plugin based consensus that can use super majority rules (2/3+1 of members of a blockchain must agree to ledger contents) or can use consensus based on parties to a transaction (e.g. supplier and user of a component).

BitCoin (an early form of blockchain) consensus used data miners (performing hard cryptographic calculations) to determine the shared state of a ledger.

There can be any number of blockchains in existence at any one time. Microsoft Azure also offers Blockchain as a service.

The potential for blockchains are enormous and very disruptive to middlemen everywhere. Anywhere ledgers are used to keep track of assets, information, money, etc, that undergo transformations, transitions or transactions as they are further refined, produced and change hands, can be easily tracked in blockchains.  The only question is can these assets, information, currency, etc. be digitally fingerprinted and can that fingerprint be read/verified. If such is the case, then blockchains can be used to track them.

New uses for Blockchain

img_6995IBM showed a demo of their new supply chain management service based on z Systems blockchain in action.  IBM component suppliers record when they shipped component(s), shippers would record when they received the component(s), port authorities would record when components arrived at port, shippers would record when parts cleared customs and when they arrived at IBM facilities. Not sure if each of these transitions were recorded, but there were a number of records for each component shipment from supplier to IBM warehouse. This service is live and being used by IBM and its component suppliers right now.

Leanne Kemp, CEO Everledger, presented another example at IBM Edge (presumably built on z Systems Hyperledger service) used to track diamonds from mining, to cutter, to polishing, to wholesaler, to retailer, to purchaser, and beyond. Apparently the diamonds have a digital bar code/fingerprint/signature that’s imprinted microscopically on the diamond during processing and can be used to track diamonds throughout processing chain, all the way to end-user. This diamond blockchain is used for fraud detection, verification of ownership and digitally certify that the diamond was produced in accordance of the Kimberley Process.

Everledger can also be used to track any other asset that can be digitally fingerprinted as they flow from creation, to factory, to wholesaler, to retailer, to customer and after purchase.

Why z System blockchains

What makes z Systems a great way to implement blockchains is its securely, isolated partitioning and advanced cryptographic capabilities such as z System functionality accelerated hashing, signing & securing and hardware based encryption to speed up blockchain processing.  z Systems also has FIPS-140 level 4 certification which can provide the highest security possible for blockchain and other security based operations.

From IBM’s perspective blockchains speak to the advantages of the mainframe environments. Blockchains are compute intensive, they require sophisticated cryptographic services and represent formal systems of record, all traditional strengths of z Systems.

Aside from the service offering, IBM has made numerous contributions to the Hyperledger project. I assume one could just download the z Systems code and run it on any LinuxONE processing environment you want. Also, since Hyperledger is Linux based, it could just as easily run in any OpenPower server running an appropriate version of Linux.

Blockchains will be used to maintain the system of record of the future just like mainframes maintained the systems of record of today and the past.

Comments?

 

Scality’s Open Source S3 Driver

img_6931
The view from Scality’s conference room

We were at Scality last week for Cloud Field Day 1 (CFD1) and one of the items they discussed was their open source S3 driver. (Videos available here).

Scality was on the 25th floor of a downtown San Francisco office tower. And the view outside the conference room was great. Giorgio Regni, CTO, Scality, said on the two days a year it wasn’t foggy out, you could even see Golden Gate Bridge from their conference room.

Scality

img_6912As you may recall, Scality is an object storage solution that came out of the telecom, consumer networking industry to provide Google/Facebook like storage services to other customers.

Scality RING is a software defined object storage that supports a full complement of interface legacy and advanced protocols including, NFS, CIGS/SMB, Linux FUSE, RESTful native, SWIFT, CDMI and Amazon Web Services (AWS) S3. Scality also supports replication and erasure coding based on object size.

RING 6.0 brings AWS IAM style authentication to Scality object storage. Scality pricing is based on usable storage and you bring your own hardware.

Giorgio also gave a session on the RING’s durability (reliability) which showed they support 13-9’s data availability. He flashed up the math on this but it was too fast for me to take down:)

Scality has been on the market since 2010 and has been having a lot of success lately, having grown 150% in revenue this past year. In the media and entertainment space, Scality has won a lot of business with their S3 support. But their other interface protocols are also very popular.

Why S3?

It looks as if AWS S3 is becoming the defacto standard for object storage. AWS S3 is the largest current repository of objects. As such, other vendors and solution providers now offer support for S3 services whenever they need an object/bulk storage tier behind their appliances/applications/solutions.

This has driven every object storage vendor to also offer S3 “compatible” services to entice these users to move to their object storage solution. In essence, the object storage industry, like it or not, is standardizing on S3 because everyone is using it.

But how can you tell if a vendor’s S3 solution is any good. You could always try it out to see if it worked properly with your S3 application, but that involves a lot of heavy lifting.

However, there is another way. Take an S3 Driver and run your application against that. Assuming your vendor supports all the functionality used in the S3 Driver, it should all work with the real object storage solution.

Open source S3 driver

img_6916Scality open sourced their S3 driver just to make this process easier. Now, one could just download their S3server driver (available from Scality’s GitHub) and start it up.

Scality’s S3 driver runs ontop of a Docker Engine so to run it on your desktop you would need to install Docker Toolbox for older Mac or Windows systems or run Docker for Mac or Docker for Windows for newer systems. (We also talked with Docker at CFD1).

img_6933Firing up the S3server on my Mac

I used Docker for Mac but I assume the terminal CLI is the same for both.Downloading and installing Docker for Mac was pretty straightforward.  Starting it up took just a double click on the Docker application, which generates a toolbar Docker icon. You do need to enter your login password to run Docker for Mac but once that was done, you have Docker running on your Mac.

Open up a terminal window and you have the full Docker CLI at your disposal. You can download the latest S3 Server from Scality’s Docker hub by executing  a pull command (docker pull scality/s3server), to fire it up, you need to define a new container (docker run -d –name s3server -p 8000:8000 scality/s3server) and then start it (docker start s3server).

It’s that simple to have a S3server running on your Mac. The toolbox approach for older Mac’s and PC’S is a bit more complicated but seems simple enough.

The data is stored in the container and persists until you stop/delete the container. However, there’s an option to store the data elsewhere as well.

I tried to use CyberDuck to load some objects into my Mac’s S3server but couldn’t get it to connect properly. I wrote up a ticket to the S3server community. It seemed to be talking to the right port, but maybe I needed to do an S3cmd to initialize the bucket first – I think.

[Update 2016Sep19: Turns out the S3 server getting started doc said you should download an S3 profile for Cyberduck. I didn’t do that originally because I had already been using S3 with Cyberduck. But did that just now and it now works just like it’s supposed to. My mistake]

~~~~

Anyways, it all seemed pretty straight forward to run S3server on my Mac. If I was an application developer, it would make a lot of sense to try S3 this way before I did anything on the real AWS S3. And some day, when I grew tired of paying AWS, I could always migrate to Scality RING S3 object storage – or at least that’s the idea.

Comments?