Learning Machine Learning – part 2

In Learning Machine Learning – part 1, we covered AWS and GCP tutorials on machine learning within each of their clouds. In part 2 we cover Microsoft tutorial(s) on machine learning in Azure.

I found Machine Learning Jump Start in Microsoft Visual Academy with instructors, Buck Woody and Seayoung Rhee. This is a series of 4 video tutorials on Azure ML Studio. ML studio seems similar to AWS SageMaker as it’s a framework to perform machine learning.

Azure (and probably AWS & GCP) have a number of methods to perform machine learning. ML studio happens to be the one that I found but there are many others worth examining.

Azure’s ML Studio tutorial videos were a better than AWS but not as good as GCP IMHO for learning machine learning.  There are four videos in the series. I watched  the first (~45 minutes), the second  (~45 minutes) and most of the third (only 25 of ~45 minutes).

Video 1 Concepts and setting up a ML Studio account

In the first video, the instructors took a long time to get going and then when  got someplace interesting, it was all play acting (human as a machine learner) to teach concepts.

The tutorials do distinguish between Supervised learning and Unsupervised learning. Both of which can apply to prediction or classification types of problems or outcomes. These are discussed as classic machine learning characteristics.

 

In the last 1/3 of the first video they discuss Azure ML Studio. It provides a common place to work and collaborate across team members. It also provides a graphical approach to machine learning. ML Studio also supports a programmable API, but I never got to that section in my viewing.

Some Azure ML Studio strengths:

  1. It provides industry recognized  data sets and data science algorithms that can be used as a black box, such as recommendation engines.
  2. It allows you to publish and consume machine learning solutions.

On the Azure portal there’s a machine learning studio icon (it’s now buried under the 100+ services link, in the AI + Machine Learning section). You use this to create a new ML studio workspace.

Inside a workspace you can use Azure ML studio services.  In the workspace you can review all your experiments (these are algorithms or predictive models being worked). 

In the Experiments page you can create a new experiment which is sort of a graphical workflow of the machine learning task.

There you will find a list of Azure sample data sets and sample algorithms that can be used in your experiment. The first video didn’t go into much detail on any of this other than showing you how to get started and create a ML studio workspace.

Video 2 how to use ML studio

Video 2 takes your ML studio workspace and runs a rudimentary experiment with it. In this video they walk you through selecting a data set, selecting algorithms to use and how to connect them into a machine learning workflow.

Creating an ML Studio experiment is almost like flowcharting your workflow. You select the data you want and drop it into the workflow. Next select an extraction engine you want to use and drop it into the work flow and connect it to the data. Then. you identify what you want to do with the data (like training) and drop that algorithm into the workflow and so on.  In the end you have defined a sequence of actions to perform on data.

In their example, dataset they use a user movie ratings dataset. They connect this to a bayesian learning model and to a IMDB database to extract movie titles. The tutorial experiment is a movie recommendation engine.Although it wasn’t a neural net many of the same techniques apply.

 

ML Studio uses an intuitive graphical approach to defining a machine learning workflow.

Video 3 publishing your ML Studio web service

Video 3 shows you how to publish (on Azure’s Marketplace) the recommendation engine created in video 2 as an OData web service.

I stopped watching the 3rd video after about 25 minutes as it was setting up various aspect of the OData web service to be deployed on Azure marketplace.

Using Azure ML studio seemed pretty straightforward. But it was much more data science/data analytics activity than neural network training.

The Azure MVA ML Studio tutorial was created in 2014 so some of the concepts are a bit dated but most still apply.

Looking today on the Azure Portal, I was still able to find the ML studio workspaces under one of the 10 AI + Machine Learning services.  Again I would have to say the GCP tutorial was a better fit for what I wanted which was how do I create a neural  net and get it trained.

Other ML approaches under Azure

There are other Azure approaches to machine learning and tutorials that support them. For example, there’s a quick start tutorial to understand how to use Python and Jupyter notebooks under Azure, which is probably closer to the neural net training in GCP.

I found myself skipping ahead a lot in video 1 as it was mainly about concepts and not much technical detail. Video 2 was a good intro into ML studio and Video 3 showed you how to publish a ML studio web service in Azure but it was more details than I wanted to know. I never got to video 4, which probably talked about ML Studio’s programable API.

If I had to do it over again, I probably would have viewed the quick start tutorial with Python and Jupyter notebooks, which sounded more like the GCP tutorials in the part 1 post.

On the other hand, Azure ML Studio tutorials supplied a good complement to the GCP tutorial, as a different (more graphical) way to do ML. It would probably be worthwhile to view before taking the AWS Sagemaker tutorials as it’s a bit higher level and quicker introduction into the workflow of AI and machine learning.

Comments?

Picture credit(s): Screen shots of Videos 1, 2 and 3 in the MVA series, (c) Microsoft 

Learning machine learning – part 1

Saw an article this past week from AWS Re:Invent that they just released their Machine Learning curriculum and materials  free to the public. Google (Cloud Platform and elsewhere) TensorFlow,  (Facebook’s) PyTorch, and Microsoft Azure CNTK frameworks  education is also available and has been for awhile now.

My money is on PyTorch and Tensorflow as being the two frameworks most likely to succeed. However all the above use many open source facilities and there seems to be a lot of cross breeding across them. Both AWS ML solutions and Microsoft CNTK offer PyTorch and TensorFlow frameworks/APIs as one option among many others.  

AWS Machine Learning

I spent about an hour plus looking over the AWS SageMaker tutorial videos in the developer section of AWS machine learning curriculum. Signing up was fairly easy but I already had an AWS login. You also had to enroll/register for the course on your AWS login  but once that was through, you could access courses.

In the comments on the AWS blog post there were a number of entries indicating broken links and other problems but I didn’t have any issues. Then again, I didn’t start at the beginning, only looked at over one series of courses, and was using the websites one week after they were announced at Re:Invent.

Amazon SageMaker is an overarching framework that can be used to perform machine learning on AWS, all the way from gathering, analyzing and modifying the dataset(s), to training the model, to creating a inference engine available as an endpoint that can be used to perform the inferencing.

Amazon also has special purpose API based tools that allow customers to embed intelligence (inferencing) directly into their application, without needing to perform the ML training. These include:

  • Amazon Recognition which provides image (facial and other tagging) recognition services
  • Amazon Polly which provides text to speech services in multilple languages, and
  • Amazon Lex which provides speech recognition technology (used by Alexa) and together with Polly helps embed conversational interfaces into customer applications.

TensorFlow Machine Learning

In the past I looked over the TensorFlow tutorials and recently rechecked them out. I found them much easier to follow this time.

 

The Google IO 2018 video on TensorFlowGetting Started With TensorFlow High Level APIs, takes you through a brief introduction to the Colab(oratory),  a GCP solution that uses TensorFlow and how to use Tensorflow Keras, tf.data and TensorFlow Eager Execution to create machine learning models and perform machine learning.

 Keras on TensorFlow seems to be the easiest approach to  use machine learning technologies. The video spends most of the time discussing a Colab Keras code element,  ~9 lines, that loads a image classification dataset, defines a 1 level (one standard layer and one output layer), trains it, validates it and uses it to perform  inferencing.

The video also touches a bit on tf.data and TensorFlow Eager Execution but the main portion discusses the 9 line TensorFlow Keras machine learning example.

Both Colab and AWS Sagemaker use and discuss Jupyter Notebooks. These appear to be an open source approach to documenting and creating a workflow and executing Python code automatically.

GCP Colab is essentially a GCP-Google Drive based Jupyter notebook execution engine. With Colab you create a Jupyter notebook on google drive and interactively execute it under Colab. You can download your Juyiter notebook files and essentially execute them anywhere else that supports TensorFlow (that supports TensorFlow v1.7 or above, with Keras API support).

In the video, the Google IO   instructors (Josh Gordon and Lawrence Moroney) walk you through building a model to recognize handwritten digits and outputs a classification (0..9) of what the handwritten digit represents.

It uses a standard labeled handwriting to digits labeled data set, called the MNIST database of handwritten digits that’s already been broken up into a training set and a validation set. Josh calls this the “Hello World” of machine learning.

The instructor in the video walks you through the (Jupyter Notebook – Eager Execution-Keras) code that inputs the data set (line 2), builds a 1 level (really two layer, one neural net layer and one output layer) neural network model (lines 3-6), trains the model (line 7), tests/validates the model (line 8) and then uses it to perform an inference (line 9).

Josh spends a little time discussing neural networks and model optimizations and some of the other parameters used in the code above. He has a few visualizations of what this all means but for the most part, the code uses a simple way to build a neural net model and some standard optimization techniques for the network.

He then goes on to discuss tf.data which is an API that can be used to create machine learning datasets and provide this data to the neural net for training or inferencing.  Apparently tf.data has a number of nifty features that allow you to take raw data and transform it into something that can be used to feed neural nets. For example, separating the data into batches, shuffling (randomizing) the batches of data, pre-fetching it so as to not starve the GPU matrix multipliers, etc.

Then it goes into how machine learning is different than regular coding. And show how TensorFlow Eager Execution is really just like Python execution. They go through another example (larger) of machine learning, this one distinguishes between cats and dogs. While they use an open source Python IDE ,  PyCharm, to test and walk through their TF Eager Execution code, setting breakpoints and examining data along the way.

At the end of the video they show a link to a Google crash course on TensorFlow machine learning and they refer to a book Deep Learning with Python by Francois Chollet. They also mention a browser version of TensorFlow which uses Java Script and  your browser to develop, train and perform inferences using TensorFlow Keras machine learning.

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Never got around to Microsoft’s Azure training other than previewing some websites but plan to look over that soon.

I would have to say that the Google IO session on using TensorFlow high level APIs was a lot more enjoyable (~40 minutes) than the AWS multiple tutorial videos (>>40 minutes) that I watched to learn about SageMaker.

Not a fair comparison as one was a Google IO intro session on TensorFlow high level APIs and the other was a series of actual training videos on Amazon SageMaker and the AWS services you can use to take advantage of it.

But the GCP session left me thinking I can handle learning more and using machine learning (via TensorFlow, Keras, Eager Execution, & tf.data) to actually do something while the SageMaker sessions left me thinking, how much AWS facilities and AWS infrastructure services,  I would need to understand and use to ever get to actually developing a machine learning model.

I suppose one was more of an (AWS SageMaker) infrastructure tutorial  and the other was more of an intro into machine learning using TensorFlow wherever you wanted to execute it.

I think I’m almost ready to start creating and feeding a TensorFlow model with my handwriting and seeing if it can properly interpret it into searchable text. If it can do that, I would be a happy camper

Comments…

Photo credits: 

Screenshos from AWS Sagemaker series of tutorial video 1, 2, 3, 4 & 5, you may need a signin to view them

Screenshots from the Getting Started with TensorFlow High Level APIs YouTube video 

NetApp’s new NVMeoF/FC AFF & Cloud Data Volumes for every cloud

We attended a NetApp analyst event in their CA HQ last week and they had some interesting announcements as well other information to share. 1st up new faster ONTAP storage.

NVMeoF AFF

NetApp announced this week that their latest generation AFF (All Flash FAS) systems will support FC NVMeoF. We asked if this was just for NVMe SSDs or did it apply to all AFF media. The answer was it’s just another host interface which the customer can license for NVMe SSDs (available only on AFF F800) or SAS SSDs (A700S, A700, and A300). The only AFF not supporting the new host interface is their lowend AFF A220.

As for which NVMeoF, they only support FC at the moment, and it’s our belief that the FC NVMeoF spec is most well defined these days and the FC switch hardware (Brocade-Broadcom since Gen 5, now shipping Gen 6, Cisco not sure) already has NVMeoF support.

NetApp also mentioned support for 100GbE (A800 & A700S only) and 32Gbs FC hardware (all AFF systems but A220). So, presumably they offer NVMeoF for both 32Gbps and 16Gbps FC.

No word on when this will be available for Ethernet FCoE or iSCSI (iNVMe?) but with all the major storage vendors bar one, moving to NVMe SSDs it’s only a matter of time before they also support Ethernet NVMeoF.

As for AFF NVMeoF performance, the answer wasn’t entirely satisfactory. The indication was that the interface reduced response time by 10 usecs or so for NVMe SSDs over SAS SSDs. But I didn’t see any other performance information to substantiate that.

We did see on their AFF datasheet that with NVMe SSDs and NVMeoF FC, the AFF A800 response time was sub 200usec with throughput of 300GB/s (in a 24 node cluster, 12 HA pairs). This means they add only about 100usec for ONTAP data services, a decent trade off from our perspective. Later in their datasheet they say the A800 is capable of 1.3M IOPS and sub-500usec latencies. Unsure why they quoted both numbers.

Cloud Data Volumes

NetApp is taking storage to the cloud. They just announced that NetApp Cloud Data Volumes will be available as a native service under Google Cloud Platform (GCP). NetApp Cloud Data Volume is a storage-as-a-service offering that provides on demand ONTAP file services in the cloud.

For GCP,  both Google and NetApp will be offering the service. Dianne Green, GCP VP said Cloud Data Volumes are a bit like Kubernetes, disruption without disrupting. Customers can easily migrate their onprem file based applications to the cloud without having to worry about the performance of their data or data protection for that matter.

Getting the data there is another matter, but NetApp has other services like CloudSync and someday (maybe for Cloud Data Volumes), SnapMirror, which can help customers move data to and from the cloud.

Currently Cloud Data Volumes are in public preview as an Microsoft Azure Enterprise NFS (and SMB) service. It’s also in beta (I think) in AWS marketplace. And availability on GCP is still restricted. There’s a lot of emphasis at NetApp events on Cloud Data Volumes given its current status on public cloud providers but we think they are trying to gain some experience before they roll it out to the rest of the world.

However,  Jean English, NetApp CMO mentioned that NetApp’s Cloud Data Service business unit has over 1800 customers and currently supports a multi-PB storage footprint in various clouds. Note, this is not just Cloud Data Volumes but comprises all NetApp Cloud Data Services, which includes ONTAP Cloud, NPS, CloudSync, AltaVault, etc. Nonetheless, it’s an impressive indicator of just how far they have come in applying their storage magic to the public cloud in a short time. The hyperscalers (read public cloud providers) say NetApp is 2 or more years ahead of all the other competition and from what we can see, it’s true.

One of the key differentiators between NetApp Cloud Data Volumes and ONTAP Cloud is performance SLAs. Cloud Data Volume customers can select and purchase a specified performance SLA. We believe it comes at three levels and is normally purchased on a pay as you go, consumption based, service offering. However, it’s also available to be billed periodically, other purchase options may be available as well.

When asked what storage was behind the service, the only thing NetApp would confirm was that it was ONTAP storage, present in public cloud data centers in various regions. So Cloud Data Volumes is available in only specific regions but I would expect that to expand over time.

Data Visualization Center

They also christened their new Data Visualization Center (DVC) and we had a multi-course meal at the Bistro at the center. The DVC had a wrap around, 1.5 floor tall screen which showed some of NetApp customer success stories. Inside the screen was a more immersive setting and there was plenty of VR equipment in work spaces alongside customer conference rooms.

Full Disclosure: NetApp paid for all our travel, hotel and food during the analyst event and gave us all Google Home Minis as going away presents and NetApp is a long time customer of my firm.

Ethereum enters the enterprise

Earth globe within a locked cage

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

 

Blockchains at IBM

Earth globe within a locked cage

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?

 

Cloud storage growth is hurting NAS & SAN storage vendors

Strange Clouds by michaelroper (cc) (from Flickr)
Strange Clouds by michaelroper (cc) (from Flickr)

My friend Alex Teu (@alexteu), from Oxygen Cloud wrote a post today about how Cloud Storage is Eating the World Alive. Alex reports that all major NAS and SAN storage vendors lost revenue this year over the previous year ranging from a ~3% loss to over a 20% loss (Q1-2014 compared to Q1-2013, from IDC).

Although an interesting development, it’s hard to say that this is the end of enterprise storage as we know it.  I believe there are a number of factors that are impacting  enterprise storage revenues and Cloud storage adoption may be only one of them.

Other trends impacting NAS & SAN storage adoption

One thing that has emerged over the last decade or so is the advance of Flash storage. Some of this is used in storage controllers to speed up IO access and some is used in servers to speed up IO access. But any speedup of IO could potentially reduce the need for high-performing disk drives and could allow customers to use higher capacity/slower disk drives instead. This could definitely reduce the cost of storage systems. A little bit of flash goes  long way to speed up IO access.

The other thing is that disk capacity is trending upward, at exponential rates. Yesterday,s 2TB disk drive is todays 4TB disk drive and we are already seeing 6TB from Seagate, HGST and others. And this is also driving down the cost of NAS and SAN storage.

Nowadays you can configure 1PB of storage with just over 170 drives. Somewhere in there you might want a couple 100TB of Flash to speed up IO access to these slow disks but Flash is also coming down in ($/GB) price (see SanDISK’s recent consumer grade TLC drive at $0.44/GB). Also the move to MLC flash has increased the capacity of flash devices, leading to less SSDs/flash cache cards to store/speed up more data.

Finally, the other trend which seems to have emerged recently is the movement away from enterprise class storage to server storage. One can see this in VMware’s VSAN, HyperConverged systems such as Nutanix and Scale Computing, as well as a general trend in Windows Server applications (SQL Server, Exchange Server, etc.) to make better use of DAS storage. So some customers are moving their data to shared DAS storage today, whereas before this was more difficult to accomplish effectively and because of that they previously purchased networked storage.

What about cloud storage?

Yes, as Alex has noted, the price of cloud storage has declined precipitously over the last year or so. Alex’s cloud storage pricing graph is shows how the entry of Microsoft and Google has seemingly forced Amazon to match their price reductions. But the other thing of note is that they have all come down to about the same basic price of $0.024/GB/Month.

It’s interesting that Amazon delayed their first S3 serious price reductions by about 4 months after Azure and Google Cloud Storage dropped there’s and then within another month after that, they all were at price parity.

What’s cloud storage real growth?

I reported last August that Microsoft Azure and Amazon S3 were respectively storing 8 trillion and over 2 trillion objects (see my Is object storage outpacing structured and unstructured data growth). This year (April 2014) Microsoft mentioned at TechEd that Azure was storing 20 Trillion object and servicing 2 million request per second.

I could find no update to Amazon S3 numbers from last year but the 10x  2.5x growth in Azure’s object count in ~8 months and the roughly doubling of request/second (In my post I didn’t mention last year they were processing 900K requests/second) say something interesting is going on in cloud storage.

I suppose Google’s cloud storage service is too new to report serious results and maybe Amazon wants to keep their growth a secret. But considering Amazon’s recent matching of Azure’s and Google’s pricing, it probably means that their growth wasn’t what they expected.

The other interesting item from the Microsoft discussions on Azure, was that they were already hosting 1M SQL databases in Azure and that 57% of Fortune 500 customers are currently using Azure.

In the “olden days”, before cloud storage, all these SQL databases and Fortune 500 data sets would have more than likely resided on NAS or SAN storage of some kind. And possibly due to the traditional storage’s higher cost and greater complexity, some of this data would never have been spun up in the first place if they had to use traditional storage, but with cloud storage so cheap, rapidly configurable and easy to use all this new data was placed in the cloud.

So I must conclude from Microsofts growth numbers and their implication for the rest of the cloud storage industry that maybe Alex was right, more data is moving to the cloud and this is impacting traditional storage revenues.  With IDC’s (2013) data growth at ~43% per year, it would seem that Microsoft’s cloud storage is growing more rapidly than the worldwide data growth, ~14X faster!

On the other hand, if cloud storage was consuming most of the world’s data growth, it would seem to precipitate the collapse of traditional storage revenues, not just a ~3-20% decline. So maybe the most new cloud storage applications would never have been implemented before if they had to use traditional storage, which means that only some of this new data would ever have been stored on traditional storage in the first place, leading to a relatively smaller decline in revenue.

One question remains: is this a short term impact or more of a long running trend that will play out over the next decade or so? From my perspective, new applications spinning up on non-traditional storage is a long running threat to traditional NAS and SAN storage which will ultimately see traditional storage relegated to a niche. How big this niche will ultimately be and how well it can be defended needs to be the subject for another post?

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Comments?

Who’s the next winner in data storage?

Strange Clouds by michaelroper (cc) (from Flickr)
Strange Clouds by michaelroper (cc) (from Flickr)

“The future is already here – just not evenly distributed”, W. Gibson

It starts as it always does outside the enterprise data center. In the line of businesses, in the development teams, in the small business organizations that don’t know any better but still have an unquenchable need for data storage.

It’s essentially an Innovator’s Dillemma situation. The upstarts are coming into the market at the lower end, lower margin side of the business that the major vendors don’t seem to care about, don’t service very well and are ignoring to their peril.

Yes, it doesn’t offer all the data services that the big guns (EMC, Dell, HDS, IBM, and NetApp) have. It doesn’t offer the data availability and reliability that enterprise data centers have come to demand from their storage. require. And it doesn’t have the performance of major enterprise data storage systems.

But what it does offer, is lower CapEx, unlimited scaleability, and much easier to manage and adopt data storage, albeit using a new protocol. It does have some inherent, hard to get around problems not the least of which is speed of data ingest/egress, highly variable latency and eventual consistency. There are other problems which are more easily solvable, with work, but the three listed above are intrinsic to the solution and need to be dealt with systematically.

And the winner is …

It has to be cloud storage providers and the big elephant in the room has to be Amazon. I know there’s a lot of hype surrounding AWS S3 and EC2 but you must admit that they are growing, doubling year over year. Yes it is starting from a much lower capacity point and yes, they are essentially providing “rentable” data storage space with limited or even non-existant storage services. But they are opening up whole new ways to consume storage that never existed before. And therein lies their advantage and threat to the major storage players today, unless they act to counter this upstart.

On AWS’s EC2 website there must be 4 dozen different applications that can be fired up in the matter of a click or two. When I checked out S3 you only need to signup and identify a bucket name to start depositing data (files, objects). After that, you are charged for the storage used, data transfer out (data in is free), and the number of HTTP GETs, PUTs, and other requests that are done on a per month basis. The first 5GB is free and comes with a judicious amount of gets, puts, and out data transfer bandwidth.

… but how can they attack the enterprise?

Aside from the three systemic weaknesses identified above, for enterprise customers they seem to lack enterprise security, advanced data services and high availability storage. Yes, NetApp’s Amazon Direct addresses some of the issues by placing enterprise owned, secured and highly available storage to be accessed by EC2 applications. But to really take over and make a dent in enterprise storage sales, Amazon needs something with enterprise class data services, availability and security with an on premises storage gateway that uses and consumes cloud storage, i.e., a cloud storage gateway. That way they can meet or exceed enterprise latency and services requirements at something that approximates S3 storage costs.

We have talked about cloud storage gateways before but none offer this level of storage service. An enterprise class S3 gateway would need to support all storage protocols, especially block (FC, FCoE, & iSCSI) and file (NFS & CIFS/SMB). It would need enterprise data services, such as read-writeable snapshots, thin provisioning, data deduplication/compression, and data mirroring/replication (synch and asynch). It would need to support standard management configuration capabilities, like VMware vCenter, Microsoft System Center, and SMI-S. It would need to mask the inherent variable latency of cloud storage through memory, SSD and hard disk data caching/tiering.. It would need to conceal the eventual consistency nature of cloud storage (see link above). And it would need to provide iron-clad, data security for cloud storage.

It would also need to be enterprise hardened, highly available and highly reliable. That means dually redundant, highly serviceable hardware FRUs, concurrent code load, multiple controllers with multiple, independent, high speed links to the internet. Todays, highly-available data storage requires multi-path storage networks, multiple-independent power sources and resilient cooling so adding multiple-independent, high-speed internet links to use Amazon S3 in the enterprise is not out of the question. In addition to the highly available and serviceable storage gateway capabilities described above it would need to supply high data integrity and reliability.

Who could build such a gateway?

I would say any of the major and some of the minor data storage players could easily do an S3 gateway if they desired. There are a couple of gateway startups (see link above) that have made a stab at it but none have it quite down pat or to the extent needed by the enterprise.

However, the problem with standalone gateways from other, non-Amazon vendors is that they could easily support other cloud storage platforms and most do. This is great for gateway suppliers but bad for Amazon’s market share.

So, I believe Amazon has to invest in it’s own storage gateway if they want to go after the enterprise. Of course, when they create an enterprise cloud storage gateway they will piss off all the other gateway providers and will signal their intention to target the enterprise storage market.

So who is the next winner in data storage – I have to believe its going to be and already is Amazon. Even if they don’t go after the enterprise which I feel is the major prize, they have already carved out an unbreachable market share in a new way to implement and use storage. But when (not if) they go after the enterprise, they will threaten every major storage player.

Yes but what about others?

Arguably, Microsoft Azure is in a better position than Amazon to go after the enterprise. Since their acquisition of StorSimple last year, they already have a gateway that with help, could be just what they need to provide enterprise class storage services using Azure. And they already have access to the enterprise, already have the services, distribution and goto market capabilities that addresses enterprise needs and requirements. Maybe they have it all but they are not yet at the scale of Amazon. Could they go after this – certainly, but will they?

Google is the other major unknown. They certainly have the capability to go after enterprise cloud storage if they want. They already have Google Cloud Storage, which is priced under Amazon’s S3 and provides similar services as far as I can tell. But they have even farther to go to get to the scale of Amazon. And they have less of the marketing, selling and service capabilities that are required to be an enterprise player. So I think they are the least likely of the big three cloud providers to be successful here.

There are many other players in cloud services that could make a play for enterprise cloud storage and emerge out of the pack, namely Rackspace, Savvis, Terremark and others. I suppose DropBox, Box and the other file sharing/collaboration providers might also be able to take a shot at it, if they wanted. But I am not sure any of them have enterprise storage on their radar just yet.

And I wouldn’t leave out the current major storage, networking and server players as they all could potentially go after enterprise cloud storage if they wanted to. And some are partly there already.

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